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{
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"baseline": "generic: current state of the art",
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"date": "2025-10",
"notes": "Unquantified superlative ('orders of magnitude') about the fabricated X0 test circuits; no number, baseline, or benchmark given.",
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{
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"claim": "achieving up to 1000× energy consumption efficiency on targeted AI and scientific workloads",
"quantified": false,
"figure": "up to 1000x energy efficiency (roadmap projection)",
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"baseline": "generic: conventional approaches / GPUs (implied, not named)",
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"workload": "\"targeted AI and scientific workloads\" (linear algebra, stochastic sampling, diffusion models) - class named, no specific benchmark",
"source_url": "https://www.prnewswire.com/news-releases/normal-computing-announces-tape-out-of-worlds-first-thermodynamic-computing-chip-302527154.html",
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"notes": "CN101 tape-out release (Aug 12, 2025), billed as 'the world's first thermodynamic computing chip.' The 1000x figure is an unattributed company assertion about potential efficiency, not a measured product figure; no named baseline system or benchmark.",
"harvest": "2026-08-23"
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{
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"bucket": "1 Thermodynamic",
"claim": "We will run AI models there, and we will have a network cable where prompts come in and inferences go out, but it'll be done at 1/1000 of power.",
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"notes": "Founder Naveen Rao's launch post announcing Unconventional, Inc. Aspirational, unquantified. (The 'O(1,000x) lower power consumption than digital computers' figure often cited for this company appears on investor a16z's announcement page, not in company materials, so it is excluded here.)",
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"notes": "Company homepage copy as accessed 2026-08; forward-looking ('will recover'), entirely qualitative. Site also states 'Maximum compute, minimal heat.' and 'computing method generates negligible amounts of heat' - no numbers anywhere on the site.",
"harvest": "2026-08-23"
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{
"company": "Vaire Computing",
"bucket": "1 Thermodynamic",
"claim": "Earley said that while Ice River's resonator demonstrated 1.77x energy recovery versus a traditional circuit, given multiple wafer runs, the [company expects improvement - sentence truncated in snippet].",
"quantified": true,
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"baseline": "generic: a traditional (non-energy-recovering) circuit",
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"notes": "EE Times quoting CTO Hannah Earley on the Ice River test chip (announced March 2025). Notably disciplined: a measured silicon result with stated conditions (22nm CMOS, energy-recovery factor >1 = net recovery), small honest numbers rather than a marketing multiplier. Coded quantified=true because it is a measured physical ratio with stated measurement conditions, though the comparison circuit is generic. EE Times page itself is bot-blocked; quote text taken from the article's Google-indexed snippet.",
"harvest": "2026-08-23"
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{
"company": "Quantum Computing Inc (QCi)",
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"claim": "EmuCore spec sheet: \"Typical power consumption: 10W\"; \"Transient response rate: 10M nodes/sec\"; \"up to 8,192 internal nodes.\" \"It is fast, affordable, energy-efficient, and brings the power of a standalone edge computing to your desktop.\"",
"quantified": true,
"figure": "10 W typical power; 10M nodes/sec; 8,192 internal nodes",
"baseline_stated": false,
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"source_url": "https://quantumcomputinginc.com/products/commercial-products/emucore",
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"date": "2024-12",
"notes": "Absolute device specs with physical units, but no comparison to any baseline and no benchmark. Date approximate (product page live through 2024-2026; EmuCore introduced ~2023). QCi's own developer docs state EmuCore 'is designed to emulate how a photonic reservoir computer could function' - i.e. the shipping device is an FPGA-based emulation of a physical (photonic) reservoir, not the photonic reservoir itself.",
"harvest": "2026-08-23"
},
{
"company": "Quantum Computing Inc (QCi)",
"bucket": "2 Reservoir",
"claim": "\"QCi's EmuCore reservoir computing device can be applied to solve a variety of problems related to serial data structures including time series prediction and image recognition in a fast, affordable, and energy-efficient manner\"; the next-generation device \"will offer enhanced performance per watt for deployment in demanding AI and sensor-rich environments.\"",
"quantified": false,
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"notes": "PR for sale of an EmuCore unit to a major automotive manufacturer (Apr 22 2025). Entirely qualitative: 'fast', 'energy-efficient', 'enhanced performance per watt', 'promising potential for AI model training' - no numbers, baselines, or benchmarks anywhere in the release.",
"harvest": "2026-08-23"
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{
"company": "D-Wave",
"bucket": "3 Quantum",
"claim": "D-Wave's quantum computer performed the most complex simulation in minutes and with a level of accuracy that would take nearly one million years using the supercomputer.",
"quantified": true,
"figure": "minutes vs ~1,000,000 years time-to-solution",
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"baseline": "Frontier supercomputer at DOE Oak Ridge National Laboratory (GPU-cluster classical simulation)",
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"workload": "magnetic materials (quantum spin-glass) simulation, published in Science (King et al. 2025)",
"source_url": "https://www.dwavequantum.com/company/newsroom/press-release/beyond-classical-d-wave-first-to-demonstrate-quantum-supremacy-on-useful-real-world-problem/",
"source_type": "press release",
"date": "2025-03",
"notes": "Flagship 'quantum supremacy on a useful problem' claim. Classical-simulation groups (e.g. tensor-network teams at Flatiron/EPFL) published rebuttals claiming comparable classical results; the claim's classical baseline is an extrapolation, not a run to completion.",
"harvest": "2026-08-23"
},
{
"company": "D-Wave",
"bucket": "3 Quantum",
"claim": "The release states the simulation \"would require more than the world's annual electricity consumption to solve this problem using the supercomputer, which is built with graphics processing unit (GPU) clusters.\"",
"quantified": false,
"figure": "more than the world's annual electricity consumption",
"baseline_stated": true,
"baseline": "Frontier supercomputer (GPU clusters), Oak Ridge National Laboratory",
"workload_stated": true,
"workload": "same magnetic-materials spin-glass simulation as above",
"source_url": "https://www.dwavequantum.com/company/newsroom/press-release/beyond-classical-d-wave-first-to-demonstrate-quantum-supremacy-on-useful-real-world-problem/",
"source_type": "press release",
"date": "2025-03",
"notes": "Energy comparison is an extrapolated estimate of a classical computation that was never actually run; no stated number+unit, so coded unquantified despite the named baseline.",
"harvest": "2026-08-23"
},
{
"company": "IonQ",
"bucket": "3 Quantum",
"claim": "We estimate that in certain cases, our Tempo systems will perform quantum calculations that would otherwise require up to 1 billion GPUs to simulate, while consuming dramatically less energy and occupying a much smaller physical footprint.",
"quantified": false,
"figure": "up to 1 billion GPUs",
"baseline_stated": false,
"baseline": "generic: GPUs",
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"source_url": "https://investors.ionq.com/news/news-details/2025/IonQ-Achieves-Record-Breaking-Quantum-Performance-Milestone-of-AQ-64/default.aspx",
"source_type": "press release",
"date": "2025-09",
"notes": "Quote from Niccolo de Masi, Chairman and CEO, in the #AQ 64 press release. Explicitly an estimate ('in certain cases'), no named GPU model, no benchmark condition, no energy figure.",
"harvest": "2026-08-23"
},
{
"company": "IonQ",
"bucket": "3 Quantum",
"claim": "With each increment in #AQ value, the useful computational space for running quantum algorithms doubles. A system with #AQ 64 is capable of considering more than 18 quintillion (i.e., 2^64) different possibilities.",
"quantified": true,
"figure": "#AQ 64 (algorithmic qubits benchmark score); 2^64 state space",
"baseline_stated": false,
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"workload_stated": true,
"workload": "#AQ (algorithmic qubits) benchmark, IonQ's suite derived from QED-C application benchmarks (QAOA, QFT, amplitude-estimation-class circuits)",
"source_url": "https://investors.ionq.com/news/news-details/2025/IonQ-Achieves-Record-Breaking-Quantum-Performance-Milestone-of-AQ-64/default.aspx",
"source_type": "press release",
"date": "2025-09",
"notes": "#AQ is IonQ's self-defined metric; the score is a real measured benchmark result on the Tempo development system, but the '18 quintillion possibilities' framing is marketing arithmetic on the metric, not a speedup claim. PR also cites 35% (QAOA), 74% (QFT), 182% (FAA) algorithm improvements vs its prior systems.",
"harvest": "2026-08-23"
},
{
"company": "Multiverse Computing",
"bucket": "3 Quantum",
"claim": "CompactifAI compresses LLMs by up to 95% with only 2-3% precision loss.",
"quantified": true,
"figure": "up to 95% size reduction with 2-3% precision loss",
"baseline_stated": true,
"baseline": "original uncompressed open-source LLMs (Llama, DeepSeek, Mistral models named in the release)",
"workload_stated": true,
"workload": "LLM compression of named open-source models (e.g. Llama 3.1-8B per companion April 2025 announcement; LlaMA-7B benchmark in arXiv:2401.14109)",
"source_url": "https://multiversecomputing.com/resources/multiverse-computing-raises-usd215m-to-scale-ground-breaking-technology-that-compresses-llms-by",
"source_type": "press release",
"date": "2025-06",
"notes": "Flagship claim of the $215M Series B announcement; quantum-inspired (tensor-network) software, not quantum hardware. Company also publishes a peer-reviewable arXiv paper backing the compression figures — unusually disciplined for this tier.",
"harvest": "2026-08-23"
},
{
"company": "Multiverse Computing",
"bucket": "3 Quantum",
"claim": "CompactifAI models are highly-compressed versions of leading open source LLMs that retain original accuracy, are 4x-12x faster and yield a 50%-80% reduction in inference costs.",
"quantified": false,
"figure": "4x-12x faster; 50-80% inference cost reduction",
"baseline_stated": true,
"baseline": "original non-compressed versions of the same LLMs",
"workload_stated": false,
"workload": null,
"source_url": "https://multiversecomputing.com/resources/multiverse-computing-raises-usd215m-to-scale-ground-breaking-technology-that-compresses-llms-by",
"source_type": "press release",
"date": "2025-06",
"notes": "Relative baseline is well-defined (the uncompressed model) but the PR states no measurement/serving conditions for the speed and cost multipliers, so coded unquantified under the strict rule.",
"harvest": "2026-08-23"
},
{
"company": "EnCharge AI",
"bucket": "4 In-memory/CIM",
"claim": "M.2 for Laptops: Delivering up to 200+ TOPS of AI compute power in an 8.25W power envelope, EN100 M.2 enables sophisticated AI applications on laptops without compromising battery life or portability.",
"quantified": true,
"figure": "200+ TOPS at 8.25 W",
"baseline_stated": false,
"baseline": null,
"workload_stated": false,
"workload": null,
"source_url": "https://www.anzupartners.com/2025/05/29/encharge-ai-announces-en100-first-of-its-kind-ai-accelerator-for-on-device-computing/",
"source_type": "press release",
"date": "2025-05",
"notes": "EN100 launch PR (2025-05-29), also on BusinessWire (20250529108055). Absolute TOPS+watts spec, no baseline or benchmark stated.",
"harvest": "2026-08-23"
},
{
"company": "EnCharge AI",
"bucket": "4 In-memory/CIM",
"claim": "20x Higher efficiency (TOPS/W)* ... 9x Higher compute density (TOPS/mm2)* [*silicon measured MACs vs. industry-leading products]",
"quantified": false,
"figure": "20x (TOPS/W); 9x (TOPS/mm2)",
"baseline_stated": false,
"baseline": "generic: industry-leading products",
"workload_stated": false,
"workload": null,
"source_url": "https://www.enchargeai.com/",
"source_type": "company page",
"date": "2026-08",
"notes": "Homepage stat tiles (accessed 2026-08-23). Footnote states 'silicon measured MACs vs. industry-leading products' -- a measurement condition but no named baseline product, so coded unquantified per rules. Same page: '200 AI TOPS at Only ~8 Watts', '10x Lower TCO (inferences/$ or tokens/$)', '100x Lower CO2 emissions (vs. Cloud)'.",
"harvest": "2026-08-23"
},
{
"company": "EnCharge AI",
"bucket": "4 In-memory/CIM",
"claim": "\"~20x better performance per watt\" than leading digital alternatives and \"orders-of-magnitude higher compute efficiency and density compared to today's best-in-class solutions.\"",
"quantified": false,
"figure": "~20x performance per watt",
"baseline_stated": false,
"baseline": "generic: leading digital alternatives / today's best-in-class solutions",
"workload_stated": false,
"workload": null,
"source_url": "https://www.businesswire.com/news/home/20250213156276/en/EnCharge-AI-Closes-Oversubscribed-$100M-Series-B-Funding-Round-Led-by-Tiger-Global-to-Produce-Transformative-AI-Accelerator-Solutions-with-Unprecedented-Efficiency-for-Client-Computing",
"source_type": "press release",
"date": "2025-02",
"notes": "Series B PR framing (as quoted in CompanyTaxonomy profile encharge-ai.md; BusinessWire blocked direct fetch). Bare multiplier + orders-of-magnitude language, no named baseline.",
"harvest": "2026-08-23"
},
{
"company": "Mythic",
"bucket": "4 In-memory/CIM",
"claim": "120 TOPS/W Industry leading energy efficiency",
"quantified": true,
"figure": "120 TOPS/W",
"baseline_stated": false,
"baseline": null,
"workload_stated": false,
"workload": null,
"source_url": "https://mythic.ai/",
"source_type": "company page",
"date": "2026-08",
"notes": "Homepage stat (accessed 2026-08-23). Physical unit stated; no baseline or workload. Refers to next-gen APU with SST memBrain cells (per Microchip/SST cross-promotion).",
"harvest": "2026-08-23"
},
{
"company": "Mythic",
"bucket": "4 In-memory/CIM",
"claim": "100x more energy-efficient than industry standard GPUs, Mythic's [APUs] ... \"100x vs. GPU Performance per watt at system level\"",
"quantified": false,
"figure": "100x energy efficiency vs GPUs",
"baseline_stated": false,
"baseline": "generic: industry standard GPUs",
"workload_stated": false,
"workload": null,
"source_url": "https://mythic.ai/whats-new/mythic-to-challenge-ais-gpu-pantheon-with-100x-energy-advantage-and-oversubscribed-125m-raise/",
"source_type": "press release",
"date": "2025-12",
"notes": "$125M raise PR (2025-12-17) lede via Google-indexed snippet; the '100x vs. GPU Performance per watt at system level' wording is verbatim from mythic.ai homepage. Bare multiplier, generic GPU baseline, no workload.",
"harvest": "2026-08-23"
},
{
"company": "Mythic",
"bucket": "4 In-memory/CIM",
"claim": "Claims air-cooled servers with \"two orders of magnitude better TCO per token\" than Nvidia Blackwell-generation hardware for 70B models.",
"quantified": false,
"figure": "~100x TCO per token",
"baseline_stated": true,
"baseline": "Nvidia Blackwell-generation hardware",
"workload_stated": true,
"workload": "70B-parameter LLM inference (tokens)",
"source_url": "https://mythic.ai/whats-new/mythic-to-challenge-ais-gpu-pantheon-with-100x-energy-advantage-and-oversubscribed-125m-raise/",
"source_type": "press release",
"date": "2025-12",
"notes": "As quoted in CompanyTaxonomy profile mythic.md from the Dec 2025 PR. Names a baseline generation and workload class, but 'orders of magnitude' = unquantified per coding rules; TCO is not a physical unit.",
"harvest": "2026-08-23"
},
{
"company": "Rain AI",
"bucket": "4 In-memory/CIM",
"claim": "We're building the most energy efficient hardware for AI",
"quantified": false,
"figure": null,
"baseline_stated": false,
"baseline": null,
"workload_stated": false,
"workload": null,
"source_url": "https://rain.ai",
"source_type": "company page",
"date": "unknown",
"notes": "Current minimal homepage (copyright 2024, still live 2026-07). Pure unquantified superlative -- the company's only remaining public claim after its analog-memristor-to-digital-IMC pivot and 2025 distress.",
"harvest": "2026-08-23"
},
{
"company": "Rain AI",
"bucket": "4 In-memory/CIM",
"claim": "Right now it's 1000X more energy efficient than existing neural networks, Wilson [said] ... Gordon Wilson: \"So we believe we can achieve that 100X [energy efficiency improvement]\"",
"quantified": false,
"figure": "1,000x (simulation-era claim); 100x (target)",
"baseline_stated": false,
"baseline": "generic: existing neural networks / GPUs",
"workload_stated": false,
"workload": null,
"source_url": "https://johnkoetsier.com/artificial-brain-neuromorphic-chip/",
"source_type": "interview",
"date": "2022-03",
"notes": "CEO Gordon Wilson in John Koetsier interview (2022-03-08), memristor era; quote fragments via Google-indexed snippet (page 403s on direct fetch). Aspirational multipliers, never product-verified; Rain later pivoted to digital IMC and reached distress/sale-exploration by 2025.",
"harvest": "2026-08-23"
},
{
"company": "Sagence AI",
"bucket": "4 In-memory/CIM",
"claim": "Compared to industry's leading volume GPU processing the Llama2-70B large language model with performance normalized to 666K tokens/sec, Sagence technology performs with 10X lower power, 20X lower price, and 20X smaller rack space.",
"quantified": false,
"figure": "10x power, 20x price, 20x rack space (normalized to 666K tokens/sec)",
"baseline_stated": false,
"baseline": "generic: industry's leading volume GPU (unnamed; Nvidia implied)",
"workload_stated": true,
"workload": "Llama2-70B inference, normalized to 666K tokens/sec",
"source_url": "https://www.sagence-ai.com/sagence-ai-emerges-from-stealth-tackling-economic-viability-of-inference-hardware-for-generative-ai/",
"source_type": "press release",
"date": "2024-11",
"notes": "Stealth-exit PR (2024-11-19), verbatim. The most benchmark-disciplined claim in this slice short of naming the GPU: workload + normalization stated, but baseline left as 'industry's leading volume GPU', so coded unquantified per rules.",
"harvest": "2026-08-23"
},
{
"company": "Sagence AI",
"bucket": "4 In-memory/CIM",
"claim": "\"100X lower power MAC function\" ... \"Sagence AI versus Nvidia B200\" \"Normalized to a fully populated 42U rack\" ... \"5X lower price\" ... \"One rack vs. ten\"",
"quantified": false,
"figure": "100x lower MAC power; 5x lower price; 10x rack density (vs Nvidia B200)",
"baseline_stated": true,
"baseline": "Nvidia B200 (rack-level comparison graphic on homepage)",
"workload_stated": false,
"workload": null,
"source_url": "https://www.sagence-ai.com/",
"source_type": "company page",
"date": "2026-08",
"notes": "Homepage comparison graphic (accessed 2026-08-23). Names Nvidia B200 and a normalization (42U rack) but no workload/benchmark for the multipliers; MAC-power 100x has no measurement condition. Site also claims 'multiple orders of magnitude improvement in energy efficiency and cost reductions'.",
"harvest": "2026-08-23"
},
{
"company": "Semron",
"bucket": "4 In-memory/CIM",
"claim": "[Semron's technology] reduces costs and energy consumption for a given computing task by at least 20x.",
"quantified": false,
"figure": "20x energy/cost reduction",
"baseline_stated": false,
"baseline": "generic: conventional chips",
"workload_stated": false,
"workload": null,
"source_url": "https://techcrunch.com/2024/01/30/semron-wants-to-replace-chip-transistors-with-memcapacitors/",
"source_type": "funding coverage",
"date": "2024-01",
"notes": "Seed-round coverage (2024-01-30); the 20x figure is voiced by seed investor Georg Stockinger (SquareOne) characterizing Semron's technology -- company-aligned but not a company exec. CEO Kirschen's own quantified-ish quote in the same piece: 'We use this property as an enabler to deploy several hundred times the compute resources on a fixed silicon area.'",
"harvest": "2026-08-23"
},
{
"company": "Semron",
"bucket": "4 In-memory/CIM",
"claim": "researchers at Semron and the Max Planck Institute of Microstructure Physics successfully trained a computer vision model at energy efficiencies of over 3,500 TOPS/W -- 35 to 300 times higher than existing techniques.",
"quantified": true,
"figure": "3,500+ TOPS/W (8-bit)",
"baseline_stated": false,
"baseline": "generic: existing (memristive) techniques (35-300x)",
"workload_stated": false,
"workload": "computer vision model training (MNIST in the underlying paper; not named in coverage)",
"source_url": "https://techcrunch.com/2024/01/30/semron-wants-to-replace-chip-transistors-with-memcapacitors/",
"source_type": "funding coverage",
"date": "2024-01",
"notes": "Figure originates in the CTO's 2021 Nature Electronics device paper (device-level projection incl. charge recovery, 29,600 TOPS/W without stated caveats elsewhere), recycled into launch coverage as the company's flagship number. Quantified but device-level, not chip/product-level; no commercial chip exists as of mid-2026.",
"harvest": "2026-08-23"
},
{
"company": "TetraMem",
"bucket": "4 In-memory/CIM",
"claim": "We realized that we could eliminate the memory wall by using RRAM crossbar arrays for analog in-memory computing, enabling real-time, low-power AI inference.",
"quantified": false,
"figure": null,
"baseline_stated": false,
"baseline": null,
"workload_stated": false,
"workload": null,
"source_url": "https://tetramem.com/about-tetramem/",
"source_type": "company page",
"date": "2026-07",
"notes": "FINDING: TetraMem publishes no quantified public performance claim (no TOPS/W, no x-factor) in its marketing material -- searches for 'TetraMem TOPS/W' etc. return only qualitative language. The quantitative results (thousands of conductance levels) live in the founders' peer-reviewed papers, not company claims.",
"harvest": "2026-08-23"
},
{
"company": "TetraMem",
"bucket": "4 In-memory/CIM",
"claim": "We believe this approach provides a practical path to improving energy efficiency and scalability for next-generation AI systems.",
"quantified": false,
"figure": null,
"baseline_stated": false,
"baseline": null,
"workload_stated": false,
"workload": null,
"source_url": "https://www.businesswire.com/news/home/20260516556464/en/TetraMem-Announces-22nm-Multi-Level-RRAM-Analog-In-Memory-Computing-SoC-Milestone",
"source_type": "press release",
"date": "2026-05",
"notes": "Exec quote in the 22nm MLX200 SoC milestone PR (2026-05-16; verbatim via evertiq/LinkedIn mirrors). Hedged, unquantified -- notably disciplined relative to peers.",
"harvest": "2026-08-23"
},
{
"company": "Witmem",
"bucket": "4 In-memory/CIM",
"claim": "The result is 10 to 20 times lower power consumption than alternative approaches along with lower overall processor Bill of Materials (BOM) costs because external DRAM and NOR are not required.",
"quantified": false,
"figure": "10-20x lower power",
"baseline_stated": false,
"baseline": "generic: alternative approaches",
"workload_stated": false,
"workload": null,
"source_url": "https://www.microchip.com/en-us/about/news-releases/corporate/computing-in-memory-innovator-solves-speech-processing-challenge",
"source_type": "press release",
"date": "2022-03",
"notes": "Joint Microchip/WITINMEM PR (2022-02/03) on the WTM2101 speech SoC using SuperFlash memBrain; verbatim via audioxpress mirror. Includes WITINMEM CEO Shaodi Wang quote: 'We were the first to develop a computing-in-memory chip for audio in 2019, and now we have achieved another milestone with volume production...'.",
"harvest": "2026-08-23"
},
{
"company": "Witmem",
"bucket": "4 In-memory/CIM",
"claim": "compared with NPU, DSP, and MCU computing platforms, the AI computing power of WTM2101 has increased by 10-200 times",
"quantified": false,
"figure": "10-200x AI computing power",
"baseline_stated": false,
"baseline": "generic: NPU, DSP, and MCU computing platforms",
"workload_stated": false,
"workload": null,
"source_url": "https://www.youtube.com/watch?v=1bQb2epR3IE",
"source_type": "company page",
"date": "2022-06",
"notes": "Witmem Technology's own YouTube channel promo for WTM2101 (~2022; 'keyword awaken and voice recognition' context). Extremely wide multiplier range, generic baselines. Company site also claims 'first in the world to mass-produce commercial CIM chips'.",
"harvest": "2026-08-23"
},
{
"company": "BrainChip",
"bucket": "5 Neuromorphic/SNN",
"claim": "Akida Pico is \"the lowest power acceleration co-processor\" ... able to execute \"with power suitable for battery-powered operation of less than a single milliwatt.\"",
"quantified": true,
"figure": "<1 mW",
"baseline_stated": false,
"baseline": null,
"workload_stated": false,
"workload": null,
"source_url": "https://brainchip.com/brainchip-introduces-lowest-power-ai-acceleration-co-processor/",
"source_type": "press release",
"date": "2024-10",
"notes": "Akida Pico launch PR (2024-10-01), BrainChip's own site. Absolute power figure with unit; 'lowest power' superlative has no baseline. CEO Sean Hehir quote in same PR contains no numbers.",
"harvest": "2026-08-23"
},
{
"company": "BrainChip",
"bucket": "5 Neuromorphic/SNN",
"claim": "Pioneering the future of edge AI with neuromorphic computing ... Real-time intelligence that runs locally. Ultra-low power. No data leaves the device.",
"quantified": false,
"figure": null,
"baseline_stated": false,
"baseline": null,
"workload_stated": false,
"workload": null,
"source_url": "https://brainchip.com/",
"source_type": "company page",
"date": "2026-07",
"notes": "Homepage positioning (accessed 2026-07-23 per corpus profile). Flagship framing is qualitative ('ultra-low power') rather than TOPS/W numbers; capacity specs (AKD1000: 1.2M neurons / 10B synapses) are stated but not efficiency claims.",
"harvest": "2026-08-23"
},
{
"company": "Innatera",
"bucket": "5 Neuromorphic/SNN",
"claim": "Innatera's Spiking Neural Processor architecture delivers up to 500x lower energy than conventional edge AI ... \"Up to 500x lower energy and up to 100x lower latency vs conventional AI solutions.\"",
"quantified": false,
"figure": "up to 500x lower energy; up to 100x lower latency",
"baseline_stated": false,
"baseline": "generic: conventional edge AI / conventional AI solutions",
"workload_stated": false,
"workload": null,
"source_url": "https://www.innatera.com/technology/",
"source_type": "company page",
"date": "2026-07",
"notes": "Technology page (accessed 2026-08-23). 'Up to' multipliers against generic baseline, no benchmark. Seed investor btov separately claimed '10,000x higher performance/watt' -- investor framing, not counted.",
"harvest": "2026-08-23"
},
{
"company": "Innatera",
"bucket": "5 Neuromorphic/SNN",
"claim": "Ultra-low power processing enabling sensor data processing in micro- and nano-watt ranges ... Sub-millisecond responsiveness.",
"quantified": false,
"figure": "uW-nW power range; sub-ms latency",
"baseline_stated": false,
"baseline": null,
"workload_stated": false,
"workload": null,
"source_url": "https://www.innatera.com/technology/",
"source_type": "company page",
"date": "2026-07",
"notes": "Order-of-magnitude power/latency ranges with units but no specific figure, condition, or workload -- coded unquantified.",
"harvest": "2026-08-23"
},
{
"company": "SpiNNcloud",
"bucket": "5 Neuromorphic/SNN",
"claim": "18x higher energy efficiency than GPUs [metric: \"Energy Efficiency in Token/s/W\", measured on 70B Parameter Llama2 Model]",
"quantified": false,
"figure": "18x tokens/s/W vs GPUs (Llama2-70B)",
"baseline_stated": false,
"baseline": "generic: GPUs",
"workload_stated": true,
"workload": "Llama2-70B inference (tokens/s/W)",
"source_url": "https://spinncloud.com/",
"source_type": "company page",
"date": "2026-08",
"notes": "Homepage headline claim (accessed 2026-08-23). Unusually, the metric (Token/s/W) and workload (70B Llama2) are stated, but the GPU baseline is unnamed -- coded unquantified per rules.",
"harvest": "2026-08-23"
},
{
"company": "SpiNNcloud",
"bucket": "5 Neuromorphic/SNN",
"claim": "78x higher energy efficiency than GPUs",
"quantified": false,
"figure": "78x energy efficiency vs GPUs",
"baseline_stated": false,
"baseline": "generic: GPUs",
"workload_stated": false,
"workload": null,
"source_url": "https://spinncloud.com/",
"source_type": "company page",
"date": "2026-08",
"notes": "Second, larger multiplier on the same homepage (accessed 2026-08-23); the page shows both 18x and 78x figures for different comparisons (condition for the 78x not stated on the page). Placeholder-looking counters ('0M Cores') suggest a JS-rendered stats section.",
"harvest": "2026-08-23"
},
{
"company": "SynSense",
"bucket": "5 Neuromorphic/SNN",
"claim": "Speck is the world's first 'sensor-compute integrated' neuromorphic intelligent [SoC] ... [bringing] convolutional dynamic vision processing to milliwatt energy budgets.",
"quantified": false,
"figure": "milliwatt-level power",
"baseline_stated": false,
"baseline": null,
"workload_stated": false,
"workload": null,
"source_url": "https://www.synsense.ai/wp-content/uploads/2023/06/Speck-devkit-datasheet.pdf",
"source_type": "company page",
"date": "2023-06",
"notes": "Speck dev-kit datasheet (June 2023) and product page ('world's first sensing-computing integrated dynamic vision intelligence SoC ... delivers milliwatt-level [power]'). 'World's first' superlative + power class, no specific figure or benchmark.",
"harvest": "2026-08-23"
},
{
"company": "SynSense",
"bucket": "5 Neuromorphic/SNN",
"claim": "SPECK's power consumption is 100 to 1,000 times lower (<1 mW).",
"quantified": true,
"figure": "<1 mW (claimed 100-1,000x lower)",
"baseline_stated": false,
"baseline": "generic: conventional vision processing solutions",
"workload_stated": false,
"workload": null,
"source_url": "https://alibabatech.medium.com/discover-the-rising-stars-006-interview-with-synsenses-founder-qiao-ning-d8dfd296436b",
"source_type": "interview",
"date": "2021-03",
"notes": "Founder-CEO Ning Qiao in Alibaba Tech 'Rising Stars' interview (~2020-2021). Absolute <1 mW figure quantified; the 100-1,000x multiplier is generic-baseline.",
"harvest": "2026-08-23"
},
{
"company": "Syntiant",
"bucket": "5 Neuromorphic/SNN",
"claim": "Custom built to run neural workloads, the Syntiant NDP101 consumes less than 140 microwatts ... [approximately] 200x more efficiency and 20x the throughput of the typical low-power MCU solution.",
"quantified": true,
"figure": "<140 uW; 200x efficiency, 20x throughput vs MCU",
"baseline_stated": false,
"baseline": "generic: typical low-power MCU solution",
"workload_stated": false,
"workload": "always-on audio/wake-word processing (context, not a named benchmark)",
"source_url": "https://www.syntiant.com/press-release/syntiant-ndp101-microprocessor-receives-linley-groups-analysts-choice-award/",
"source_type": "press release",
"date": "2020-01",
"notes": "Syntiant's own PR (2020-01-21); the same '~200x more efficient, 20x throughput vs typical low-power MCU' formula recurs in its 2019-2020 PRs. The <140 uW absolute figure is quantified; the multipliers are generic-baseline.",
"harvest": "2026-08-23"
},
{
"company": "Syntiant",
"bucket": "5 Neuromorphic/SNN",
"claim": "the NDP100 and NDP101 microwatt-level processors achieve 100x efficiency and 10x the throughput over traditional CPUs and DSPs",
"quantified": false,
"figure": "100x efficiency, 10x throughput vs CPUs/DSPs",
"baseline_stated": false,
"baseline": "generic: traditional CPUs and DSPs",
"workload_stated": false,
"workload": null,
"source_url": "https://www.syntiant.com/press-release/syntiant-surpasses-milestone-of-10-million-processors-shipped/",
"source_type": "press release",
"date": "2021-01",
"notes": "Syntiant's own PR (2021-01-12). Note the internal inconsistency: 200x-vs-MCU in 2019-2020 PRs becomes 100x-vs-CPUs/DSPs in 2020-2021 PRs -- same chips, shifting baseline class and multiplier.",
"harvest": "2026-08-23"
},
{
"company": "Lightelligence",
"bucket": "6 Photonic",
"claim": "\"PACE runs this particular Ising problem algorithm hundreds of times faster than a typical computing unit, such as NVIDIA RTX® 3080.\"",
"quantified": false,
"figure": "\"hundreds of times\" faster",
"baseline_stated": true,
"baseline": "NVIDIA RTX 3080",
"workload_stated": true,
"workload": "Ising problem (combinatorial optimization)",
"source_url": "https://www.prnewswire.com/news-releases/lightelligence-demo-harnesses-light-to-tackle-some-of-the-hardest-computational-problems-301444813.html",
"source_type": "press release",
"date": "2021-12",
"notes": "Company PR, Dec 15 2021. Baseline and workload are named, but the multiplier itself is vague ('hundreds of times'), hence quantified=false. CSIS later summarized the advertised claim as '25-100x faster than NVIDIA's industry-leading GPU.'",
"harvest": "2026-08-23"
},
{
"company": "Lightelligence",
"bucket": "6 Photonic",
"claim": "The PACE demo runs \"25 times faster than other algorithms, such as the Simulated Bifurcation Machine, for the selected Ising problem.\"",
"quantified": true,
"figure": "25x faster",
"baseline_stated": true,
"baseline": "Toshiba Simulated Bifurcation Machine",
"workload_stated": true,
"workload": "selected Ising problem instance",
"source_url": "https://www.prnewswire.com/news-releases/lightelligence-demo-harnesses-light-to-tackle-some-of-the-hardest-computational-problems-301444813.html",
"source_type": "press release",
"date": "2021-12",
"notes": "Same PR. Multiplier tied to a named baseline system and a stated problem class, so coded quantified=true, with the caveat that the workload is a single company-selected Ising instance; PACE never became the shipping business (company pivoted to optical interconnect).",
"harvest": "2026-08-23"
},
{
"company": "Lightmatter",
"bucket": "6 Photonic",
"claim": "Lightmatter's Blade system reportedly delivers \"3 times higher inferences/second than the Nvidia DGX-A100 with 7 times the inferences/second/Watt on BERT-Base with the SQuAD dataset.\"",
"quantified": true,
"figure": "3x inferences/second; 7x inferences/second/Watt",
"baseline_stated": true,
"baseline": "NVIDIA DGX-A100",
"workload_stated": true,
"workload": "BERT-Base inference on SQuAD",
"source_url": "https://www.redsharknews.com/photonic-computers-the-future-of-computing-is-analogue",
"source_type": "launch coverage",
"date": "2021-05",
"notes": "Wording originates from Lightmatter's own Envise launch materials / Series B press release (Businesswire 20210506005271, fetch timed out); identical claim reported by SiliconANGLE 2021-05-06 ('three times higher inference performance than the DGX-A100 and seven times better power efficiency'). One of the most disciplined claims in the photonic set: named baseline + named benchmark + per-watt unit.",
"harvest": "2026-08-23"
},
{
"company": "Lightmatter",
"bucket": "6 Photonic",
"claim": "\"The website suggests it's 5x faster than an Nvidia A100 unit on a large transformer model like BERT, while using about 15% of the energy.\"",
"quantified": true,
"figure": "5x faster; ~15% of the energy",
"baseline_stated": true,
"baseline": "NVIDIA A100",
"workload_stated": true,
"workload": "BERT (large transformer) inference",
"source_url": "https://techcrunch.com/2021/05/06/lightmatters-photonic-ai-ambitions-light-up-an-80m-b-round/",
"source_type": "funding coverage",
"date": "2021-05",
"notes": "TechCrunch verbatim, explicitly characterizing Lightmatter's own website claim for Envise. AI Business (2021-03) reported the same 5x-vs-A100-on-BERT claim, adding it 'has not been independently verified.'",
"harvest": "2026-08-23"
},
{
"company": "Lightmatter",
"bucket": "6 Photonic",
"claim": "Passage M1000 offers \"114 Tbps total optical bandwidth for the most demanding AI infrastructure applications\" and is billed as the \"world's fastest AI interconnect,\" with \"an order of magnitude higher bandwidth in a smaller package size compared to conventional Co-Packaged Optics (CPO).\"",
"quantified": true,
"figure": "114 Tbps optical bandwidth (256 fibers at 448 Gbps/fiber)",
"baseline_stated": false,
"baseline": "generic: conventional co-packaged optics (CPO)",
"workload_stated": false,
"workload": null,
"source_url": "https://lightmatter.co/press-release/lightmatter-unveils-passage-m1000-photonic-superchip-worlds-fastest-ai-interconnect/",
"source_type": "press release",
"date": "2025-03",
"notes": "Post-pivot flagship claim: interconnect bandwidth spec, not compute. Bandwidth figure is a hard spec; the 'order of magnitude vs CPO' and 'world's fastest' framing are unquantified superlatives against a generic class.",
"harvest": "2026-08-23"
},
{
"company": "LightOn",
"bucket": "6 Photonic",
"claim": "\"Current specifications are setting new standards for sustainable AI acceleration, with more than 1.500 TeraOPS at about 30 W TDP.\" (LightOn Appliance OPU launch, billed as the world's first commercially available photonic co-processor.)",
"quantified": true,
"figure": "1,500 TeraOPS at ~30 W TDP (=50 TeraOPS/W)",
"baseline_stated": false,
"baseline": null,
"workload_stated": false,
"workload": null,
"source_url": "https://www.hpcwire.com/off-the-wire/lighton-unveils-lighton-appliance-photonic-co-processor/",
"source_type": "press release",
"date": "2021-03",
"notes": "Company press release (HPCwire 'Off the Wire' republication, Paris, March 9 2021). Absolute spec with physical units - but the 'OPS' are the fixed random projections the OPU performs, not general MACs, so the TeraOPS figure is not comparable to GPU TOPS. OPU-era claim; LightOn has since pivoted entirely to GenAI software.",
"harvest": "2026-08-23"
},
{
"company": "LightOn",
"bucket": "6 Photonic",
"claim": "\"Delivering 1500 TeraOPS at 30 W, the OPU is typically two orders of magnitude more energy efficient for this operation than programmable silicon chips.\"",
"quantified": true,
"figure": "1,500 TeraOPS at 30 W; \"two orders of magnitude\" energy-efficiency delta",
"baseline_stated": false,
"baseline": "generic: programmable silicon chips (GPUs/FPGAs)",
"workload_stated": true,
"workload": "large random projections (the OPU's native operation)",
"source_url": "https://arxiv.org/pdf/2107.11814",
"source_type": "whitepaper",
"date": "2021-07",
"notes": "Company-authored technical paper ('LightOn Optical Processing Unit: Scaling-up AI and HPC with a Non von Neumann co-processor', Brossollet et al., LightOn staff; presented at Hot Chips 33). quantified=true for the TeraOPS/W numbers; the 'two orders of magnitude' comparison itself is orders-of-magnitude language vs a generic class. Unusually, the operation is explicitly scoped ('for this operation' = random projections at 1.9 kHz).",
"harvest": "2026-08-23"
},
{
"company": "Lumai",
"bucket": "6 Photonic",
"claim": "Lumai's optical processing units will \"deliver 50 times the performance of silicon-only accelerators\" while \"using just 10% of the power required for AI in data centers.\"",
"quantified": false,
"figure": "50x performance; 10% of the power",
"baseline_stated": false,
"baseline": "generic: silicon-only accelerators",
"workload_stated": false,
"workload": null,
"source_url": "https://lumai.ai/resources/lumai-secures-more-than-10m-investment-2",
"source_type": "press release",
"date": "2025-04",
"notes": "Company funding announcement, Apr 2 2025. Forward-looking ('will deliver'), no product shipping as of mid-2026, no named baseline or benchmark.",
"harvest": "2026-08-23"
},
{
"company": "Lumai",
"bucket": "6 Photonic",
"claim": "Lumai says its 3D optical computing approach will \"cut AI inference costs to one-tenth of today's top solutions.\"",
"quantified": false,
"figure": "1/10th inference cost",
"baseline_stated": false,
"baseline": "generic: today's top solutions",
"workload_stated": false,
"workload": null,
"source_url": "https://lumai.ai/resources/lumai-secures-more-than-10m-investment-2",
"source_type": "press release",
"date": "2025-04",
"notes": "Cost claim (economic, not physical unit). Coverage of the same round (optics.org, Optica OPN) relayed a company-claimed potential of 10^17 operations/s, ~1000x current electronic technology - also unquantified in the strict sense.",
"harvest": "2026-08-23"
},
{
"company": "Optalysys",
"bucket": "6 Photonic",
"claim": "\"After integrating the necessary electronics over the next 18 months, he predicts that these devices should be about 500 times faster than the best electronic-only chips.\"",
"quantified": false,
"figure": "~500x faster (prediction)",
"baseline_stated": false,
"baseline": "generic: best electronic-only chips",
"workload_stated": false,
"workload": null,
"source_url": "https://www.optica-opn.org/home/articles/volume_34/january_2023/features/photonic_computing_for_sale/",
"source_type": "interview",
"date": "2023-01",
"notes": "Optica OPN feature quoting/paraphrasing Optalysys director Nick New about the Enable photonic FHE chip. A forward-looking prediction, paraphrased by the journalist rather than a direct quote, vs a generic baseline; implicitly about FHE workloads but no benchmark stated.",
"harvest": "2026-08-23"
},
{
"company": "Optalysys",
"bucket": "6 Photonic",
"claim": "Optalysys says its photonic FHE acceleration \"allows encrypted data to be processed at similar speeds to its unencrypted form\"; CEO Nick New: \"Our Enable technology allows us to turbo boost the workflows and address the underlying bottlenecks that hold FHE back.\"",
"quantified": false,
"figure": null,
"baseline_stated": false,
"baseline": "generic: plaintext (unencrypted) processing speed",
"workload_stated": true,
"workload": "fully homomorphic encryption (FHE) workloads",
"source_url": "https://tech.eu/2023/07/17/leeds-based-photonic-chip-company-optalysys-raises-ps21-million-to-unlock-its-fully-homomorphic-encryption-process/",
"source_type": "funding coverage",
"date": "2023-07",
"notes": "Series A (GBP 21M) coverage. Current optalysys.com carries NO numeric performance claims at all - only qualitative 'unlocking new levels of computing efficiency' (checked 2026-08). The quantified claims of the company's earlier optical-Fourier era have disappeared from its own materials.",
"harvest": "2026-08-23"
},
{
"company": "Q.ANT",
"bucket": "6 Photonic",
"claim": "The Q.ANT Native Processing Unit is specified to deliver \"at least 30 times greater energy efficiency and significant computational speed improvements over traditional CMOS technology.\"",
"quantified": false,
"figure": "\"at least 30x\" energy efficiency",
"baseline_stated": false,
"baseline": "generic: traditional CMOS technology",
"workload_stated": false,
"workload": null,
"source_url": "https://qant.com/press-releases/q-ant-launches-first-commercial-photonic-processor-for-energy-efficient-high-performance-computing-and-real-time-ai-applications/",
"source_type": "press release",
"date": "2024-11",
"notes": "NPU commercial launch PR, Nov 19 2024. Flagship claim: bare multiplier vs a generic technology class, no named baseline product, no stated benchmark. Same PR: photonic chips 'offer the potential to reduce the energy consumption for that query by a factor of 30.'",
"harvest": "2026-08-23"
},
{
"company": "Q.ANT",
"bucket": "6 Photonic",
"claim": "\"A conventional CMOS multiplier requires 1,200 transistors to perform a simple 8-bit multiplication, the Q.ANT NPU achieves this with a single optical element.\"",
"quantified": false,
"figure": "1,200 transistors vs 1 optical element (8-bit multiply)",
"baseline_stated": false,
"baseline": "generic: conventional CMOS multiplier",
"workload_stated": true,
"workload": "8-bit multiplication",
"source_url": "https://qant.com/press-releases/q-ant-launches-first-commercial-photonic-processor-for-energy-efficient-high-performance-computing-and-real-time-ai-applications/",
"source_type": "press release",
"date": "2024-11",
"notes": "Structural/component-count comparison rather than a measured performance figure (no TOPS/W, latency, or throughput), so coded quantified=false despite the specific numbers.",
"harvest": "2026-08-23"
},
{
"company": "Q.ANT",
"bucket": "6 Photonic",
"claim": "In Q.ANT's Kolmogorov-Arnold-Network simulations, \"43% fewer parameters are required and the number of operations can be reduced by 46%\"; for an image-recognition example the native nonlinear approach needs \"only 0.1 million parameters and 0.2 million operations\" versus 5.1M parameters and 10M operations conventionally.",
"quantified": false,
"figure": "43% fewer parameters; 46% fewer operations (0.1M vs 5.1M params)",
"baseline_stated": false,
"baseline": "generic: conventional (linear) neural-network approach",
"workload_stated": true,
"workload": "image recognition with Kolmogorov-Arnold Networks (MNIST mentioned in same PR)",
"source_url": "https://qant.com/press-releases/q-ant-launches-first-commercial-photonic-processor-for-energy-efficient-high-performance-computing-and-real-time-ai-applications/",
"source_type": "press release",
"date": "2024-11",
"notes": "Simulation-based model-size claim, not a hardware energy/speed measurement (parameters/operations, not a physical unit) - coded quantified=false under the strict rule. Notably more specific than most peers' launch claims. Separately, Q.ANT's 2026 IONOS announcement claims NPU 2 showed up to 50x performance over gen-1 in independent evaluation at Leibniz Supercomputing Centre (thequantuminsider.com/2026/05/21/).",
"harvest": "2026-08-23"
},
{
"company": "Salience Labs",
"bucket": "6 Photonic",
"claim": "Salience Labs' photonic processors are designed to perform the matrix multiplication at the core of AI \"in a single pass rather than through multiple clock cycles,\" enabling dense chips clocking at tens of GHz.",
"quantified": false,
"figure": "\"tens of GHz\" (vague); single-pass matmul",
"baseline_stated": false,
"baseline": "generic: electronic processors / clock-cycle execution",
"workload_stated": false,
"workload": null,
"source_url": "https://www.businesswire.com/news/home/20220512005064/en/",
"source_type": "press release",
"date": "2022-05",
"notes": "From the $11.5M seed press release (Businesswire fetches timed out; the quoted single-pass phrase is reproduced verbatim in third-party mirrors, e.g. thecompute100.com/private/companies/salience-labs). No number with a unit, no baseline, no workload. NOTABLE ABSENCE: no quantified public performance claim (TOPS, TOPS/W, x-vs-named-GPU) was findable for Salience Labs in either its accelerator era or its 2025 optical-switch era ($30M Series A, businesswire.com/news/home/20250207290130/en/, claims only 'ultra-low latency' qualitatively).",
"harvest": "2026-08-23"
},
{
"company": "InfinityQ",
"bucket": "7 Probabilistic/Ising",
"claim": "TitanQ boasts up to 1000 times faster solving capabilities compared to classical solvers, making it a game-changer for a multitude of applications.",
"quantified": false,
"figure": "up to 1000x faster vs classical solvers",
"baseline_stated": false,
"baseline": "generic: classical solvers",
"workload_stated": false,
"workload": "combinatorial optimization (no named problem instance or benchmark in the claim)",
"source_url": "https://www.globenewswire.com/news-release/2024/02/22/2833817/0/en/unleashing-titanq-revolutionizing-problem-solving-with-quantum-inspired-computing.html",
"source_type": "press release",
"date": "2024-02",
"notes": "infinityQ Technology's own TitanQ launch release (Feb 22, 2024). Launch coverage (Inside Quantum Technology, Feb 23, 2024) restates it as: 'Built around an advanced “Ising Machine,” TitanQ leverages quantum mechanics principles to deliver solutions up to 1000 times faster than traditional methods.' Bare multiplier, generic baseline, no named benchmark.",
"harvest": "2026-08-23"
},
{
"company": "LightSolver",
"bucket": "7 Probabilistic/Ising",
"claim": "As a purely optical system, the LPU100 overcomes the performance barriers of electronics and can execute mathematical operations at unprecedented speed. For example, a vector matrix multiplication (VMM), which is an operation in optimization and various other applications, can be executed by the LPU100 in as little as 10 nanoseconds, compared to a GPU which needs a few microseconds for the same operation.",
"quantified": true,
"figure": "VMM in ~10 ns vs 'a few microseconds' on a GPU (~100-1000x)",
"baseline_stated": false,
"baseline": "generic: a GPU (no named product)",
"workload_stated": true,
"workload": "vector-matrix multiplication (single operation, not an end-to-end benchmark)",
"source_url": "https://www.hpcwire.com/off-the-wire/lightsolver-lpu100-laser-computing-system-empowers-enterprises-to-solve-the-toughest-optimization-problems/",
"source_type": "press release",
"date": "2024-04",
"notes": "LightSolver's own LPU100 release (HPCwire Off-the-Wire, 'Source: LightSolver', April 2, 2024). Has a physical unit (10 ns latency) and a named operation, but the GPU baseline is generic and the comparison is per-operation, not a solved-problem benchmark.",
"harvest": "2026-08-23"
},
{
"company": "LightSolver",
"bucket": "7 Probabilistic/Ising",
"claim": "Its LPU100 system unleashes the power of 100 lasers to solve the toughest optimization problems, challenging the processing times of quantum and supercomputers. The LPU100's laser array represents 100 continuous variables, and can tackle problems with up to 120^100 combinations.",
"quantified": false,
"figure": "100 lasers / 100 continuous variables; search space 'up to 120^100 combinations' (capacity spec, not a performance measurement)",
"baseline_stated": false,
"baseline": "generic: quantum computers and supercomputers",
"workload_stated": false,
"workload": "optimization problems (unspecified)",
"source_url": "https://www.hpcwire.com/off-the-wire/lightsolver-lpu100-laser-computing-system-empowers-enterprises-to-solve-the-toughest-optimization-problems/",
"source_type": "press release",
"date": "2024-04",
"notes": "Same LPU100 release. The hardware capacity numbers are concrete, but the speed claim ('challenging the processing times of quantum and supercomputers') carries no measurement. Exponent formatting: source text renders as '120100', i.e. 120^100.",
"harvest": "2026-08-23"
},
{
"company": "LightSolver",
"bucket": "7 Probabilistic/Ising",
"claim": "This technological achievement is already proving more scalable and practical than quantum computers and supercomputers, and we are excited to share its power with our customers through the cloud-based LightSolver platform.",
"quantified": false,
"figure": null,
"baseline_stated": false,
"baseline": "generic: quantum computers and supercomputers",
"workload_stated": false,
"workload": null,
"source_url": "https://www.hpcwire.com/off-the-wire/lightsolver-lpu100-laser-computing-system-empowers-enterprises-to-solve-the-toughest-optimization-problems/",
"source_type": "press release",
"date": "2024-04",
"notes": "Direct quote from LightSolver CEO and co-founder Ruti Ben Shlomi in the company's LPU100 release. Unquantified superiority claim over two entire technology classes.",
"harvest": "2026-08-23"
},
{
"company": "Knowm Inc",
"bucket": "8 AHaH (Knowm)",
"claim": "kT-RAM promises power efficiency gains of up to 10 orders of magnitude over traditional computing architectures. In the same way that GPUs greatly accelerated graphics applications, kT-RAM will accelerate machine learning applications.",
"quantified": false,
"figure": "up to 10 orders of magnitude power efficiency",
"baseline_stated": false,
"baseline": "generic: traditional computing architectures",
"workload_stated": false,
"workload": null,
"source_url": "https://knowm.org/knowm-inc-official-launch-press-release/",
"source_type": "press release",
"date": "2015-07",
"notes": "Official launch press release. 'Orders of magnitude' with a generic baseline and no workload — coded exactly like every other unquantified superlative in this dataset. The GPU analogy is aspirational framing, not a measurement.",
"harvest": "2026-08-23"
},
{
"company": "Knowm Inc",
"bucket": "8 AHaH (Knowm)",
"claim": "Once we develop and perfect AHaH based methods that achieve primary performance parity with existing methods like backprop, Knowm will dominate in secondary metrics such as power efficiency, speed, and cost.",
"quantified": false,
"figure": null,
"baseline_stated": false,
"baseline": "generic: existing machine-learning methods (backprop) on conventional hardware",
"workload_stated": false,
"workload": null,
"source_url": "https://knowm.org/faq/",
"source_type": "company page",
"date": "unknown",
"notes": "FAQ page, ca. 2015-2019. Notably candid framing: it concedes primary performance parity with state-of-the-art ML is a goal not yet achieved, and the efficiency dominance claim is explicitly conditional on that future achievement — a projection, not a benchmark.",
"harvest": "2026-08-23"
},
{
"company": "Knowm Inc",
"bucket": "8 AHaH (Knowm)",
"claim": "The energy per memristor per synaptic read or write event is then 5E-7W x 50E-9s = 2.5E-14 Joules... This is .1 pico-joules per adaptive synaptic event.",
"quantified": true,
"figure": "~2.5e-14 J (~0.1 pJ) per adaptive synaptic event",
"baseline_stated": false,
"baseline": null,
"workload_stated": false,
"workload": null,
"source_url": "https://knowm.org/faq/",
"source_type": "company page",
"date": "unknown",
"notes": "A per-device energy calculation from stated device operating parameters (J/op with units) — quantified under the rule, but it is a back-of-envelope projection from device parameters, not a measured system benchmark on any workload, and no comparison system is named.",
"harvest": "2026-08-23"
},
{
"company": "CATALOG (Catalog Technologies)",
"bucket": "9 Molecular/DNA",
"claim": "In September, using an innovative combinatorial writing scheme, CATALOG encoded approximately 17,000 words from Shakespeare's Hamlet into DNA in a few minutes on Shannon, CATALOG's flagship writer. On this DNA archive, CATALOG performed a parallel search computation.",
"quantified": true,
"figure": "~17,000 words encoded in a few minutes",
"baseline_stated": false,
"baseline": null,
"workload_stated": true,
"workload": "keyword search (finding occurrences of a query word) over a Hamlet text corpus encoded in DNA",
"source_url": "https://www.globenewswire.com/news-release/2022/12/12/2571782/0/en/catalog-achieves-historic-dna-computing-milestone.html",
"source_type": "press release",
"date": "2022-12",
"notes": "A demonstrated milestone with a real (tiny) workload, but it is a data-search demo, not an AI/ML performance claim — no comparison to any electronic system's speed or energy is made. Coded quantified on the stated throughput figure; note the workload is search, not learning/inference.",
"harvest": "2026-08-23"
},
{
"company": "CATALOG (Catalog Technologies)",
"bucket": "9 Molecular/DNA",
"claim": "Catalog Technologies says it has made a historic breakthrough by demonstrating massively parallel search of data stored in DNA. CATALOG has proved the ability to search data stored in DNA in a massively parallel and scalable manner with resource usage almost independent of the data size.",
"quantified": false,
"figure": null,
"baseline_stated": false,
"baseline": null,
"workload_stated": true,
"workload": "parallel search over DNA-stored data",
"source_url": "https://www.blocksandfiles.com/data-management/2022/12/12/catalog-demos-massively-parallel-dna-storage-search/1600342",
"source_type": "launch coverage",
"date": "2022-12",
"notes": "First sentence is coverage attributing the claim to the company ('says'); second sentence is the equivalent claim from the Cambridge Consultants (engineering partner) project page describing CATALOG's result. 'Massively parallel', 'scalable', 'almost independent of data size' — no numbers, no baseline. CATALOG has since pivoted messaging toward DNA-based computation for analytics/ML but has published no AI-workload performance figures.",
"harvest": "2026-08-23"
},
{
"company": "SEEQC",
"bucket": "11 Superconducting",
"claim": "SEEQC's digital chip technology utilizes energy-efficient superconducting SFQ logic, operating at speeds up to 40 GHz, to implement classical qubit control.",
"quantified": true,
"figure": "up to 40 GHz logic speed",
"baseline_stated": false,
"baseline": null,
"workload_stated": false,
"workload": null,
"source_url": "https://www.hpcwire.com/off-the-wire/seeqc-unveils-italys-1st-quantum-computing-system/",
"source_type": "press release",
"date": "unknown",
"notes": "Company boilerplate reused across SEEQC press releases (this instance: Italy system launch PR republished by HPCwire off-the-wire, ca. 2023). A clock-speed spec with a unit, not a benchmarked comparison — 'energy-efficient' itself carries no number.",
"harvest": "2026-08-23"
},
{
"company": "SEEQC",
"bucket": "11 Superconducting",
"claim": "Integrating all core functions of a quantum computer on a digital chip, at the same temperature as the qubits — unlocking a clear path to scaling qubits without scaling complexity.",
"quantified": false,
"figure": null,
"baseline_stated": false,
"baseline": null,
"workload_stated": false,
"workload": null,
"source_url": "https://seeqc.com/",
"source_type": "company page",
"date": "unknown",
"notes": "Current homepage positioning (accessed 2026-08). SEEQC's own site carries no quantified performance/efficiency numbers — no power, cost, or wiring-reduction figures. SEEQC is quantum-control-focused; it makes no direct AI-workload performance claims.",
"harvest": "2026-08-23"
},
{
"company": "Snowcap Compute",
"bucket": "11 Superconducting",
"claim": "Snowcap believes that even after accounting for energy used in cooling, its chips will be about 25 times better than today's best chips in terms of performance per watt.",
"quantified": false,
"figure": "~25x performance per watt",
"baseline_stated": false,
"baseline": "generic: today's best chips",
"workload_stated": false,
"workload": null,
"source_url": "https://www.reuters.com/business/snowcap-compute-raises-23-million-superconducting-ai-chips-2025-06-23/",
"source_type": "launch coverage",
"date": "2025-06",
"notes": "Reuters launch coverage reporting the company's own belief (attributed to Snowcap/CEO Michael Lafferty). Projection — no silicon exists; first chips targeted for end of the decade. Bare multiplier, no named baseline product, no workload.",
"harvest": "2026-08-23"
},
{
"company": "Snowcap Compute",
"bucket": "11 Superconducting",
"claim": "Superconducting circuits use fundamentally less energy, with switching gates that use 5 orders of magnitude less energy, and wires that have zero resistance.",
"quantified": false,
"figure": "5 orders of magnitude less switching energy",
"baseline_stated": false,
"baseline": "generic: (CMOS) switching gates, implied",
"workload_stated": false,
"workload": null,
"source_url": "https://snowcapcompute.com/",
"source_type": "company page",
"date": "2025-06",
"notes": "Device-physics framing on the company homepage; 'orders of magnitude' coded unquantified per rule. No system-level benchmark exists yet.",
"harvest": "2026-08-23"
},
{
"company": "Snowcap Compute",
"bucket": "11 Superconducting",
"claim": "Snowcap is building the first commercially viable superconducting compute platform — designed to deliver dramatic gains in speed and energy efficiency for AI, quantum, and high-performance computing.",
"quantified": false,
"figure": null,
"baseline_stated": false,
"baseline": null,
"workload_stated": false,
"workload": null,
"source_url": "https://www.businesswire.com/news/home/20250623688438/en/Snowcap-Compute-Launches-with-%2423-Million-Led-by-Playground-Global-to-Power-the-Next-Era-of-AI-and-Quantum-Compute",
"source_type": "press release",
"date": "2025-06",
"notes": "Launch/funding press release ($23M led by Playground Global, ex-Intel CEO Pat Gelsinger involved). 'Dramatic gains' — unquantified superlative.",
"harvest": "2026-08-23"
},
{
"company": "Everspin Technologies",
"bucket": "12 Spintronic",
"claim": "Everspin MRAM accelerates AI inference, improves system efficiency, and ensures data integrity in AI deployments both at the edge and in embedded AI systems.",
"quantified": false,
"figure": null,
"baseline_stated": false,
"baseline": null,
"workload_stated": false,
"workload": null,
"source_url": "https://www.everspin.com/applications/ai",
"source_type": "company page",
"date": "unknown",
"notes": "Accessed 2026-08. The only commercial spintronic vendor found marketing directly at AI, and its page contains zero numbers — no latency, power, or endurance figures; MRAM is positioned as a memory component ('fast write speeds', 'instant-on', 'low-power in-memory compute functionalities'), not an AI accelerator with benchmarked performance.",
"harvest": "2026-08-23"
},
{
"company": "MemComputing",
"bucket": "13 Physical learning",
"claim": "performance improvements over a million times faster than conventional methods",
"quantified": false,
"figure": ">1,000,000x speedup vs conventional methods (company-reported; SDSC-evaluated prototype)",
"baseline_stated": false,
"baseline": "generic: conventional methods/solvers",
"workload_stated": false,
"workload": "industrial-scale combinatorial optimization problem classes (unspecified in the claim)",
"source_url": "https://memcpu.com/our-story/",
"source_type": "company page",
"date": "2026-08",
"notes": "From memcpu.com 'Our Story' as accessed 2026-08; the company attributes the benchmarking to the San Diego Supercomputer Center as independent evaluator of an early prototype (circa 2019). No named baseline solver, benchmark instance, or units in the public claim - the largest bare multiplier in this batch, from the least-capitalized company (~$3-4.5M raised).",
"harvest": "2026-08-23"
},
{
"company": "MemComputing",
"bucket": "13 Physical learning",
"claim": "Ultra-efficient AI processors deliver breakthrough speed and energy savings",
"quantified": false,
"figure": null,
"baseline_stated": false,
"baseline": null,
"workload_stated": false,
"workload": null,
"source_url": "https://memcpu.com/",
"source_type": "company page",
"date": "2026-08",
"notes": "Marketing copy for the announced MEMAI chip line (chip pivot announced Nov 2024; no shipped silicon publicly confirmed as of mid-2026). Entirely unquantified.",
"harvest": "2026-08-23"
},
{
"company": "Vivum Computing",
"bucket": "13 Physical learning",
"claim": "He has lead the scientific team at Vivum in the development of a next generation AI technology that reduces the power requirement of neural networks by 6000 [times - sentence truncated in indexed snippet]",
"quantified": false,
"figure": "6000x neural-network power reduction",
"baseline_stated": false,
"baseline": "generic: conventional neural networks (implied)",
"workload_stated": false,
"workload": null,
"source_url": "https://vivum.ai/about",
"source_type": "company page",
"date": "2026-08",
"notes": "From vivum.ai/about (bio of CTO Derek Whitley), as indexed by Google 2026-08; the page renders via JavaScript so the sentence tail is truncated in the retrievable snippet. Corroborated by CTO's personal site ('6000× Neural Net Power Reduction', derekwhitley.com) and third-party profile NeuromorphicCore.ai ('Reduces neural network power consumption by up to 6000 times'). No published benchmark methodology, baseline hardware, or workload found in any source reviewed. Sic: 'has lead' is the page's own wording.",
"harvest": "2026-08-23"
},
{
"company": "AIStorm",
"bucket": "4 In-memory/CIM",
"claim": "CHARGE DOMAIN EDGE ANALOG AI UP TO 600 TOPS/W IN 40nm. UP to 8 TOPS/mm2 IN 40nm.",
"quantified": true,
"figure": "up to 600 TOPS/W (40nm); 8 TOPS/mm2",
"baseline_stated": false,
"baseline": null,
"workload_stated": false,
"workload": null,
"source_url": "https://aistorm.ai/",
"source_type": "company page",
"date": "2025",
"notes": "Homepage banner for the charge-domain analog AI block. Absolute unit figure (TOPS/W) with a process node, but 'up to' with no stated precision, workload or comparison system.",
"harvest": "2026-09-21"
},
{
"company": "AIStorm",
"bucket": "4 In-memory/CIM",
"claim": "CHARGE DOMAIN DIGITAL COMPUTE UP TO 117X LESS POWER. UP TO 30X SMALLER. UP TO 10X HIGHER PERFORMANCE.",
"quantified": false,
"figure": "up to 117x less power / 30x smaller / 10x higher performance",
"baseline_stated": false,
"baseline": "generic: conventional (transistor-based) digital compute, implied",
"workload_stated": false,
"workload": null,
"source_url": "https://aistorm.ai/",
"source_type": "company page",
"date": "2025",
"notes": "Homepage banner. Bare multipliers with no unit, baseline system or benchmark; the comparison object is implied by the page's 'compute that runs on charge, not transistors' framing.",
"harvest": "2026-09-21"
},
{
"company": "AIStorm",
"bucket": "4 In-memory/CIM",
"claim": "captures up to 260,000 frames per second — 2,000 to 4,000 times faster than conventional CMOS sensors",
"quantified": true,
"figure": "up to 260,000 fps; 2,000-4,000x vs conventional CMOS sensors",
"baseline_stated": false,
"baseline": "generic: conventional CMOS sensors",
"workload_stated": false,
"workload": null,
"source_url": "https://www.globenewswire.com/news-release/2025/08/12/3131504/0/en/AIStorm-Tower-Semiconductor-Introduce-Cheetah-HS-World-s-First-Up-to-260K-FPS-AI-in-Imager-Chip-for-Inspection-Robotics-Sports.html",
"source_type": "press release",
"date": "2025-08",
"notes": "Cheetah HS launch PR with Tower Semiconductor (Aug 12, 2025). The absolute figure (260,000 fps) is a sensor frame-rate spec, not an AI-compute or energy metric; the multiplier is against a generic sensor class. Wording retrieved via page-reader (GlobeNewswire blocks direct fetch) - verify before publication.",
"harvest": "2026-09-21"
},
{
"company": "AlpsenTek",
"bucket": "0 Digital accelerator",
"claim": "Low power: 4 mW @ 1000fps enables true AON edge applications.",
"quantified": true,
"figure": "4 mW at 1000 fps (ALPIX-Maloja APX002)",
"baseline_stated": false,
"baseline": null,
"workload_stated": false,
"workload": null,
"source_url": "https://en.alpsentek.com/new/high-cost-efficiency-evs-apx002/",
"source_type": "company page",
"date": "2025",
"notes": "ALPIX-Maloja (APX002) launch post. Event-based vision SENSOR, not a compute substrate - coded '0 Digital accelerator' per instruction (event sensors), for the user to decide. Absolute power figure with an operating condition (1000 fps); no comparison system or task.",
"harvest": "2026-09-21"
},
{
"company": "AlpsenTek",
"bucket": "0 Digital accelerator",
"claim": "Low data redundancy & low computation load: generates only 10-20% of the data volume of equivalent frame-based sensors.",
"quantified": false,
"figure": "10-20% of frame-based data volume",
"baseline_stated": false,
"baseline": "generic: equivalent frame-based sensors",
"workload_stated": false,
"workload": null,
"source_url": "https://en.alpsentek.com/new/high-cost-efficiency-evs-apx002/",
"source_type": "company page",
"date": "2025",
"notes": "Relative data-volume claim against a generic sensor class; no absolute unit or benchmark. Event sensor - see bucket note above.",
"harvest": "2026-09-21"
},
{
"company": "Ambient Scientific",
"bucket": "4 In-memory/CIM",
"claim": "The GPX10 Pro is built on Ambient Scientific's proprietary DigAn silicon architecture offering up to 100x improvements in power, performance and area compared to conventional 32-bit microcontrollers.",
"quantified": false,
"figure": "up to 100x power/performance/area vs conventional 32-bit MCUs",
"baseline_stated": false,
"baseline": "generic: conventional 32-bit microcontrollers",
"workload_stated": false,
"workload": null,
"source_url": "https://embeddedcomputing.com/technology/processing/chips-and-socs/ambient-scientific-launches-gpx10-pro-ai-native-soc-for-battery-powered-edge-devices",
"source_type": "press release (via trade coverage)",
"date": "2025-09",
"notes": "GPX10 Pro launch (Sept 2025); trade article reproduces the release text ('According to the press release'). Bare 'up to 100x' against a generic MCU class; no unit, product baseline or benchmark.",
"harvest": "2026-09-21"
},
{
"company": "Ambient Scientific",
"bucket": "4 In-memory/CIM",
"claim": "Peak perf (GOPs) 512 0.24 100 Active Power 1 (uW) 40-120 200 10,000 Efficiency 3 (TOPS/Watt) 7.3 1.2 <1",
"quantified": true,
"figure": "512 GOPS peak; 40-120 uW active; 7.3 TOPS/W (GPX10) vs 1.2 (ASIC) and <1 (MCU+NPU)",
"baseline_stated": false,
"baseline": "generic: 'ASIC Chip' and 'MCU with built-in NPU' columns, unnamed",
"workload_stated": false,
"workload": null,
"source_url": "https://www.ambientscientific.ai/product",
"source_type": "company page",
"date": "2026",
"notes": "Product-page comparison table ('GPX10 vs. Competitors', Sensor Fusion AI rows), quoted as the page text renders it: columns are Ambient GPX10 | ASIC Chip | MCU with built-in NPU; footnote '1 - For Sensor Fusion AI Applications'. Absolute TOPS/W and uW figures, but the comparison columns are unnamed product classes and no benchmark is given.",
"harvest": "2026-09-21"
},
{
"company": "Ambient Scientific",
"bucket": "4 In-memory/CIM",
"claim": "The GPX10 Pro shows what's possible when you build your architecture natively for AI – 100s of GOPs of AI performance at microwatts of power.",
"quantified": false,
"figure": "100s of GOPS at microwatts",
"baseline_stated": false,
"baseline": null,
"workload_stated": false,
"workload": null,
"source_url": "https://embeddedcomputing.com/technology/processing/chips-and-socs/ambient-scientific-launches-gpx10-pro-ai-native-soc-for-battery-powered-edge-devices",
"source_type": "exec quote (press release)",
"date": "2025-09",
"notes": "GP Singh (CEO) quote in the GPX10 Pro release. Order-of-magnitude phrasing ('100s of GOPs', 'microwatts') rather than a measured figure; no baseline or task.",
"harvest": "2026-09-21"
},
{
"company": "ANAFLASH",
"bucket": "4 In-memory/CIM",
"claim": "A 333TOPS/W Logic-Compatible Multi-Level Embedded Flash Compute-In-Memory Macro with Dual-Slope Computation",
"quantified": true,
"figure": "333 TOPS/W (eFlash CIM macro, CICC 2023)",
"baseline_stated": false,
"baseline": null,
"workload_stated": false,
"workload": null,
"source_url": "https://www.anaflash.com/news?category=announcement",
"source_type": "company page",
"date": "2023-05",
"notes": "Company news feed item 'Featured in CICC 2023' listing the paper title. The figure is a peer-reviewed macro-level (not product) TOPS/W number that the company promotes on its site; no comparison system or benchmark stated in the listing.",
"harvest": "2026-09-21"
},
{
"company": "ANAFLASH",
"bucket": "4 In-memory/CIM",
"claim": "By processing intelligence closer to where data resides, our technology mimics the efficiency of the nervous system and enables faster, lower-power AI at the edge.",
"quantified": false,
"figure": null,
"baseline_stated": false,
"baseline": null,
"workload_stated": false,
"workload": null,
"source_url": "https://www.anaflash.com/",
"source_type": "company page",
"date": "2026",
"notes": "Homepage positioning line. Pure superlative; no number, baseline or workload.",
"harvest": "2026-09-21"
},
{
"company": "AnotherBrain",
"bucket": "5 Neuromorphic/SNN",
"claim": "This needs to be done in a chip consuming a fraction of a watt versus, say, 15 or 20 watts for the inference phase of deep learning and thousands of watts for the learning phase of the training.",
"quantified": false,
"figure": "'a fraction of a watt' vs 15-20 W (DL inference) / thousands of W (training)",
"baseline_stated": false,
"baseline": "generic: deep-learning inference/training hardware, unnamed",
"workload_stated": false,
"workload": null,
"source_url": "https://eetimes.com/eu-content/anotherbrain-raises-e19m-to-speed-up-organic-ai-chip/",
"source_type": "exec quote (interview)",
"date": "2019-11",
"notes": "Bruno Maisonnier (CEO) in EE Times Europe at the EUR19M Series A. Aspirational ('needs to be done'); own figure is not absolute ('a fraction of a watt'); the numbers given are for the generic comparison class. Bucket is a judgment call: the profile files AnotherBrain as an analog-IMC-adjacent boundary case (cortex-emulating ASIC ambition, never shipped; pivoted to inspection software) - flagged for the user. Wording retrieved via page-reader.",
"harvest": "2026-09-21"
},
{
"company": "AnotherBrain",
"bucket": "5 Neuromorphic/SNN",
"claim": "Our technology is frugal in energy and data, it learns autonomously, and can explain its decisions.",
"quantified": false,
"figure": null,
"baseline_stated": false,
"baseline": null,
"workload_stated": false,
"workload": null,
"source_url": "https://anotherbrain.ai/en/organic-ai",
"source_type": "company page",
"date": "2026",
"notes": "Organic AI product page. Unquantified superlative.",
"harvest": "2026-09-21"
},
{
"company": "Applied Brain Research",
"bucket": "0 Digital accelerator",
"claim": "Full vocabulary speech-to-text and text-to-speech are now possible at under 30mW of power consumption, 10–100 times less than other available alternatives.",
"quantified": true,
"figure": "<30 mW for full-vocabulary STT/TTS; 10-100x less than alternatives",
"baseline_stated": false,
"baseline": "generic: other available alternatives",
"workload_stated": true,
"workload": "full-vocabulary speech-to-text and text-to-speech (TSP1)",
"source_url": "https://www.prnewswire.com/news-releases/applied-brain-research-closes-seed-funding-round-to-accelerate-edge-ai-voice-processing-302658022.html",
"source_type": "press release",
"date": "2026-01",
"notes": "Seed-round PR (Jan 12, 2026). Absolute power figure with a named task; the 10-100x multiplier is against an unnamed class. TSP1 is a digital state-space-model accelerator (no analog/emerging device) - coded '0 Digital accelerator'.",
"harvest": "2026-09-21"
},
{
"company": "Applied Brain Research",
"bucket": "0 Digital accelerator",
"claim": "The LMU became the first in a new class of neural network called State Space Models that are orders of magnitude more computationally efficient than popular attention-based architectures",
"quantified": false,
"figure": "orders of magnitude vs attention-based architectures",
"baseline_stated": false,
"baseline": "generic: attention-based (transformer) architectures",
"workload_stated": false,
"workload": null,
"source_url": "https://www.appliedbrainresearch.com/technology",
"source_type": "company page",
"date": "2026",
"notes": "Technology page. Algorithmic (not silicon) claim; unquantified 'orders of magnitude' against a generic architecture class.",
"harvest": "2026-09-21"
},
{
"company": "Aspinity",
"bucket": "4 In-memory/CIM",
"claim": "300 TOPS/W (INT8), test-chip verified",
"quantified": true,
"figure": "300 TOPS/W INT8 (AML200 RF test chip)",
"baseline_stated": false,
"baseline": null,
"workload_stated": false,
"workload": null,
"source_url": "https://www.aspinity.com/products.html",
"source_type": "company page",
"date": "2026",
"notes": "AML200 (analog AI for RF, 'In development - test chip validated') spec tile on the products page. Absolute unit figure; no comparison system or benchmark; product not yet shipping.",
"harvest": "2026-09-21"
},
{
"company": "Aspinity",
"bucket": "4 In-memory/CIM",
"claim": "<20µA Always-on operating current 100× Lower power than digital AI <1ms On-device inference latency 10+ yr Always-on battery life",
"quantified": true,
"figure": "<20 uA always-on; <1 ms latency; 100x lower power than digital AI",
"baseline_stated": false,
"baseline": "generic: digital AI",
"workload_stated": false,
"workload": null,
"source_url": "https://www.aspinity.com/products.html",
"source_type": "company page",
"date": "2026",
"notes": "AML100 spec tiles as rendered on the products page. The current and latency tiles are absolute figures (so quantified=yes); the '100x' tile is a bare multiplier against a generic class.",
"harvest": "2026-09-21"
},
{
"company": "Aspirare Semi",
"bucket": "4 In-memory/CIM",
"claim": "Up to 2x higher performance at 10x reduction in energy consumption compared to traditional solutions.",
"quantified": false,
"figure": "up to 2x performance / 10x energy reduction vs traditional solutions",
"baseline_stated": false,
"baseline": "generic: traditional solutions",
"workload_stated": false,
"workload": null,
"source_url": "https://www.aspirare.io/",
"source_type": "company page",
"date": "2026",
"notes": "Homepage. Bare multipliers, no unit, named baseline or benchmark; no product silicon details published.",
"harvest": "2026-09-21"
},
{
"company": "The Biological Computing Co. (TBC)",
"bucket": "10 Biological/wetware",
"claim": "Under those testing conditions, TBC's optimized OASIS model demonstrated a twofold improvement on the selected video-quality benchmark, required approximately 4.4 times less inference cost and produced more than three times as much coherent video as the base model.",
"quantified": true,
"figure": "2x video-quality benchmark; 4.4x less inference cost; >3x coherent video vs base OASIS model",
"baseline_stated": true,
"baseline": "base (unoptimized) OASIS open-source video model",
"workload_stated": true,
"workload": "text-to-video generation with OASIS on a 'selected video-quality benchmark' (benchmark not named)",
"source_url": "https://www.prnewswire.com/news-releases/the-biological-computing-co-tbc-releases-proof-of-concept-for-neurally-optimized-ai-software-302838521.html",
"source_type": "press release",
"date": "2026-07",
"notes": "Proof-of-concept PR (July 2026, corrected release). Multipliers are tied to a named baseline model and a stated test condition, so coded quantified; the benchmark itself is unnamed. NOTE: TBC's product is neuron-derived SOFTWARE running on standard GPUs ('Your video is generated by software, not living cells' - tbc.co); the wetware is used for algorithm discovery, not deployed compute. Coded '10 Biological/wetware' per instruction; flagged for the substrate chart.",
"harvest": "2026-09-21"
},
{
"company": "The Biological Computing Co. (TBC)",
"bucket": "10 Biological/wetware",
"claim": "We turn discoveries from real neurons into software that makes AI better, faster and cheaper.",
"quantified": false,
"figure": null,
"baseline_stated": false,
"baseline": null,
"workload_stated": false,
"workload": null,
"source_url": "https://www.tbc.co/",
"source_type": "company page",
"date": "2026",
"notes": "Homepage tagline; unquantified. The '23x retained improvement in video model efficiency' figure circulating for TBC appears on investor Primary.vc's blog (Feb 2026), not in a TBC-attributed sentence - excluded.",
"harvest": "2026-09-21"
},
{
"company": "Blumind",
"bucket": "4 In-memory/CIM",
"claim": "Blumind all-analog AI compute delivers deterministic and precise inferencing performance at up to x1000 lower power than our competitors.",
"quantified": false,
"figure": "up to 1000x lower power than competitors",
"baseline_stated": false,
"baseline": "generic: our competitors",
"workload_stated": false,
"workload": null,
"source_url": "https://blumind.ai/technology/",
"source_type": "company page",
"date": "2026",
"notes": "AMPL technology page. Bare multiplier, unnamed competitors, no benchmark. Same '1000x lower power than available alternatives' line appears in the Series A PR (Apr 2025).",
"harvest": "2026-09-21"
},
{
"company": "Blumind",
"bucket": "4 In-memory/CIM",
"claim": "For example, Blumind's AI neural signal processor and analog microphone based key word detection solution is lower cost and ~20x low power than digital microphone-based AI alternatives.",
"quantified": false,
"figure": "~20x lower power vs digital-microphone AI alternatives",
"baseline_stated": false,
"baseline": "generic: digital microphone-based AI alternatives",
"workload_stated": true,
"workload": "keyword detection (always-on audio)",
"source_url": "https://blumind.ai/technology/",
"source_type": "company page",
"date": "2026",
"notes": "Technology page. Task named (keyword detection) but the multiplier is bare and the baseline generic.",
"harvest": "2026-09-21"
},
{
"company": "Ceryx Medical",
"bucket": "5 Neuromorphic/SNN",
"claim": "Our studies have found that Cysoni's way of pacing the heart increased cardiac output by 20%, when compared with monotonic pacing.",
"quantified": true,
"figure": "+20% cardiac output vs monotonic pacing",
"baseline_stated": true,
"baseline": "monotonic (fixed-rate) pacing",
"workload_stated": true,
"workload": "cardiac pacing (respiratory-sinus-arrhythmia pacing), pre-clinical studies",
"source_url": "https://www.ceryxmedical.com/post/ceryx-raises-3-8m-to-bring-cardiac-device-to-in-human-trials",
"source_type": "exec quote (company post)",
"date": "2025-06",
"notes": "Dr Stuart Plant (CEO) in company funding post. This is a therapeutic-efficacy figure for an analog-silicon-neuron (central-pattern-generator) device, not a compute performance/efficiency claim - recorded because it is the company's flagship quantitative claim; bucket '5 Neuromorphic/SNN' (analog neurons) is a judgment call; flagged.",
"harvest": "2026-09-21",
"excluded_reason": "therapeutic-efficacy figure (cardiac output) for a pacemaker, not a compute claim"
},
{
"company": "Cortical Labs",
"bucket": "10 Biological/wetware",
"claim": "Designed to keep neurons alive for up to 6 months with its internal life support system, the CL1 needs minimal inputs and a fraction of energy other technologies use, enabling longer research timelines.",
"quantified": false,
"figure": null,
"baseline_stated": false,
"baseline": "generic: other technologies",
"workload_stated": false,
"workload": null,
"source_url": "https://corticallabs.com/cl1.html",
"source_type": "company page",
"date": "2026",
"notes": "CL1 product page ('Low power, to the extreme'). Unquantified; no watt figure is given on the company page (press coverage cites ~850-1,000 W per unit).",
"harvest": "2026-09-21"
},
{
"company": "Cortical Labs",
"bucket": "10 Biological/wetware",
"claim": "Unlike traditional AI, our neural systems require minimal energy and training data to master complex tasks.",
"quantified": false,
"figure": null,
"baseline_stated": false,
"baseline": "generic: traditional AI",
"workload_stated": false,
"workload": null,
"source_url": "https://corticallabs.com/",
"source_type": "company page",
"date": "2026",
"notes": "Homepage. Unquantified superlative.",
"harvest": "2026-09-21"
},
{
"company": "Crossbar, Inc.",
"bucket": "4 In-memory/CIM",
"claim": "Energy Efficient 5× less energy than eFlash, 20× less than NAND, 30× less than SPI Flash, and 40× less than BLE.",
"quantified": false,
"figure": "5x/20x/30x/40x less energy vs eFlash/NAND/SPI Flash/BLE",
"baseline_stated": false,
"baseline": "generic: memory technology classes (eFlash, NAND, SPI Flash) and BLE, no product named",
"workload_stated": false,
"workload": null,
"source_url": "https://www.crossbar-inc.com/",
"source_type": "company page",
"date": "2026",
"notes": "Homepage 'ReRAM Highlights'. Memory-IP claim (ReRAM as storage/secure element), not an AI-acceleration claim - recorded on the same basis as Everspin in the existing set. Multipliers against technology classes with no condition.",
"harvest": "2026-09-21"
},
{
"company": "Crossbar, Inc.",
"bucket": "4 In-memory/CIM",
"claim": "High Performance 20ns read, 12μs write, no erase latency. 100× faster reads, and 1,000× faster writes than traditional memory.",
"quantified": true,
"figure": "20 ns read, 12 us write; 100x/1000x vs traditional memory",
"baseline_stated": false,
"baseline": "generic: traditional memory",
"workload_stated": false,
"workload": null,
"source_url": "https://www.crossbar-inc.com/",
"source_type": "company page",
"date": "2026",
"notes": "Homepage. Absolute latency figures (quantified) plus bare multipliers. Memory-IP claim, see above.",
"harvest": "2026-09-21"
},
{
"company": "Eta Compute (now ModelCat)",
"bucket": "0 Digital accelerator",
"claim": "Always-on image processing and sensor applications can be achieved with a power budget of 100µW.",
"quantified": true,
"figure": "100 uW always-on (ECM3532)",
"baseline_stated": false,
"baseline": null,
"workload_stated": false,
"workload": null,
"source_url": "https://www.eetimes.com/endpoint-ai-in-a-matter-of-microwatts/",
"source_type": "exec quote (interview)",
"date": "2020-02",
"notes": "Ted Tewksbury (CEO) at ECM3532 production-silicon announcement. Absolute power figure; application class, not a named benchmark. Asynchronous-logic digital SoC (Cortex-M3 + DSP), not an analog/emerging substrate - coded '0'. Company later exited silicon (now ModelCat). Wording retrieved via page-reader (EE Times blocks direct fetch).",
"harvest": "2026-09-21"
},
{
"company": "Eta Compute (now ModelCat)",
"bucket": "0 Digital accelerator",
"claim": "By virtue of the fact that we have a hundred to a thousand times greater energy efficiency [than competitors]...",
"quantified": false,
"figure": "100-1000x energy efficiency vs competitors",
"baseline_stated": false,
"baseline": "generic: competitors (bracketed insertion is the article's)",
"workload_stated": false,
"workload": null,
"source_url": "https://www.eetimes.com/endpoint-ai-in-a-matter-of-microwatts/",
"source_type": "exec quote (interview)",
"date": "2020-02",
"notes": "Tewksbury quote; bare multiplier. The bracketed '[than competitors]' is EE Times' insertion. Retrieved via page-reader.",
"harvest": "2026-09-21"
},
{
"company": "femtoAI (fka Femtosense)",
"bucket": "0 Digital accelerator",
"claim": "Technical Breakthrough: femtoAI successfully shipped the femtoAI SPU-001, which sets a new standard by delivering 100x lower power consumption, 10x smaller silicon footprint and 10x faster integration.",
"quantified": false,
"figure": "100x lower power / 10x smaller / 10x faster integration",
"baseline_stated": false,
"baseline": null,
"workload_stated": false,
"workload": null,
"source_url": "https://femto.ai/press_releases/2025-12-30/",
"source_type": "press release",
"date": "2025-12",
"notes": "Year-end PR (Dec 30, 2025). Bare multipliers with no stated comparison system or benchmark. SPU-001 is a digital sparse-processing accelerator (SNN lineage, not an SNN chip) - coded '0 Digital accelerator'.",
"harvest": "2026-09-21"
},
{
"company": "femtoAI (fka Femtosense)",
"bucket": "0 Digital accelerator",
"claim": "With 200K+ chips already deployed, and customers across data center, enterprise and consumer electronics, femtoAI delivers AI inference solutions that consume 100X less power & energy with 10X less memory to make AI economically viable on every device.",
"quantified": false,
"figure": "100x less power & energy; 10x less memory",
"baseline_stated": false,
"baseline": null,
"workload_stated": false,
"workload": null,
"source_url": "https://www.prnewswire.com/news-releases/femtoai-unlocks-5x-growth-with-full-stack-ai-inference-platform-302832353.html",
"source_type": "press release",
"date": "2026-07",
"notes": "PR of July 22, 2026. Bare multipliers, no baseline or workload; the '10X less memory' is elsewhere tied to 'open-source models such as OpenAI Whisper' via model compression.",
"harvest": "2026-09-21"
},
{
"company": "FinalSpark",
"bucket": "10 Biological/wetware",
"claim": "consume a million times less power than traditional digital processors",
"quantified": false,
"figure": "1,000,000x less power vs digital processors",
"baseline_stated": false,
"baseline": "generic: traditional digital processors",
"workload_stated": false,
"workload": null,
"source_url": "https://www.tomshardware.com/pc-components/cpus/worlds-first-bioprocessor-uses-16-human-brain-organoids-for-a-million-times-less-power-consumption-than-a-digital-chip",
"source_type": "company statement (quoted in coverage)",
"date": "2024-05",
"notes": "Tom's Hardware (May 26, 2024) quoting the company ('the company says') at the Neuroplatform launch. Bare multiplier; the Neuroplatform is a 16-organoid research platform, not a deployed processor.",
"harvest": "2026-09-21"
},
{
"company": "FinalSpark",
"bucket": "10 Biological/wetware",
"claim": "Bioprocessors could be up to a million times more energy-efficient than traditional silicon chips.",
"quantified": false,
"figure": "up to 1,000,000x energy efficiency vs silicon",
"baseline_stated": false,
"baseline": "generic: traditional silicon chips",
"workload_stated": false,
"workload": null,
"source_url": "https://bioalps.org/finalsparks-neuroplatform-the-era-of-organic-computing-has-begun/",
"source_type": "exec quote (interview)",
"date": "2024-10",
"notes": "Fred Jordan (co-founder) quoted by BioAlps; conditional ('could be'), generic baseline, no task. Same figure as the launch line.",
"harvest": "2026-09-21"
},
{
"company": "Fractile",
"bucket": "0 Digital accelerator",
"claim": "Run the most advanced models up to 25x faster and at 1/10th the cost.",
"quantified": false,
"figure": "up to 25x faster, 1/10 cost",
"baseline_stated": false,
"baseline": "generic: existing inference hardware, implied",
"workload_stated": false,
"workload": null,
"source_url": "https://www.fractile.ai/",
"source_type": "company page",
"date": "2026",
"notes": "Homepage hero line (2026; earlier company messaging said 100x faster / 10x cheaper). Bare multipliers, no baseline system or model named. Digital SRAM in-memory compute with no emerging device - coded '0 Digital accelerator' (profile: 'boundary-digital'); chips not expected before 2027.",
"harvest": "2026-09-21"
},
{
"company": "GEMESYS",
"bucket": "4 In-memory/CIM",
"claim": "HIGH EFFICIENT AI TRAINING We expect the analog paradigm to increase training efficiency by the factor 10.000",
"quantified": false,
"figure": "10,000x training efficiency (expected)",
"baseline_stated": false,
"baseline": null,
"workload_stated": false,
"workload": null,
"source_url": "https://gemesys.tech/",
"source_type": "company page",
"date": "2026",
"notes": "Homepage. Forward-looking ('we expect'), bare multiplier, no unit/baseline/benchmark. Sifted (Nov 2024) reports prototypes at '100-1,000x over GPUs' - journalist paraphrase, excluded.",
"harvest": "2026-09-21"
},
{
"company": "General Vision Inc.",
"bucket": "0 Digital accelerator",
"claim": "Extreme Power Efficiency: The NM500 — performs at the equivalent of 10 Giga- Operations per milliwatt, making it an ideal companion to the low power Lattice's Nexus™ 2 FPGA platform and Lattice sensAI™ solution stack for battery-powered and thermally constrained deployments.",
"quantified": true,
"figure": "10 GOPS/mW (NM500)",
"baseline_stated": false,
"baseline": null,
"workload_stated": false,
"workload": null,
"source_url": "https://img.pr.com/release-file/2603/963525/GV_Lattice_PR_v3-9_Clean.pdf",
"source_type": "press release",
"date": "2026-03",
"notes": "Lattice Partner Network PR (Mar 18, 2026). Absolute unit figure ('equivalent of' - for an associative-memory RBF/kNN classifier, not MAC operations); no baseline or benchmark. NeuroMem is a fully digital parallel classifier (not spiking, no analog) - coded '0'.",
"harvest": "2026-09-21"
},
{
"company": "General Vision Inc.",
"bucket": "0 Digital accelerator",
"claim": "The result is a heterogeneous edge AI architecture that is faster, leaner, and more power-efficient than software-only deep learning alternatives.",
"quantified": false,
"figure": null,
"baseline_stated": false,
"baseline": "generic: software-only deep learning",
"workload_stated": false,
"workload": null,
"source_url": "https://img.pr.com/release-file/2603/963525/GV_Lattice_PR_v3-9_Clean.pdf",
"source_type": "press release",
"date": "2026-03",
"notes": "Same PR. Unquantified superlative.",
"harvest": "2026-09-21"
},
{
"company": "GrAI Matter Labs",
"bucket": "0 Digital accelerator",
"claim": "GrAI Matter Labs' programmable NeuronFlow™ technology enables industry-leading inference latency efficiently – more than an order of magnitude better than competing solutions.",
"quantified": false,
"figure": ">10x latency vs competing solutions",
"baseline_stated": false,
"baseline": "generic: competing solutions",
"workload_stated": false,
"workload": null,
"source_url": "https://www.dutchnews.nl/businesswire/grai-matter-labs-raises-14m-to-bring-fastest-ai-per-watt-to-every-device-on-the-edge/",
"source_type": "press release (BusinessWire, mirrored)",
"date": "2020-11",
"notes": "$14M round PR (Nov 23, 2020), BusinessWire text mirrored by DutchNews. Bare 'order of magnitude', unnamed competitors, no benchmark. Digital event-driven dataflow (GrAI One / VIP) - coded '0'. Company acquired by Snap (2023).",
"harvest": "2026-09-21"
},
{
"company": "GrAI Matter Labs",
"bucket": "0 Digital accelerator",
"claim": "We are excited to bring the fastest AI per Watt to every device on the edge",
"quantified": false,
"figure": null,
"baseline_stated": false,
"baseline": null,
"workload_stated": false,
"workload": null,
"source_url": "https://www.dutchnews.nl/businesswire/grai-matter-labs-raises-14m-to-bring-fastest-ai-per-watt-to-every-device-on-the-edge/",
"source_type": "exec quote (press release)",
"date": "2020-11",
"notes": "Ingolf Held (CEO). Pure superlative.",
"harvest": "2026-09-21"
},
{
"company": "Grayscale AI",
"bucket": "5 Neuromorphic/SNN",
"claim": "It combines low-power AI computing, local interpretation and analysis, and achieves negligible standby consumption, making it up to 10,000 times more efficient than its counterpart.",
"quantified": false,
"figure": "up to 10,000x more efficient than 'its counterpart'",
"baseline_stated": false,
"baseline": "generic: 'its counterpart' (frame-based/conventional vision AI, implied)",
"workload_stated": false,
"workload": null,
"source_url": "https://zenzic.io/cam-scale-up/grayscale-ai/",
"source_type": "programme profile (Zenzic CAM Scale-Up)",
"date": "n.d. (accessed 2026-09)",
"notes": "ATTRIBUTION CAVEAT: appears on Zenzic's CAM Scale-Up programme profile of Grayscale AI, which reads as company-supplied copy but is not on a Grayscale-owned page; the '500x less energy' line cited in the taxonomy profile could not be found on the Automotive World article it was attributed to. Grayscale ships neuromorphic SOFTWARE with no disclosed chip - bucket '5' is provisional. Drop if the attribution bar is strict.",
"harvest": "2026-09-21",
"excluded_reason": "attribution: quote appears on a Zenzic programme profile, not on a Grayscale-owned page"
},
{
"company": "Great Sky",
"bucket": "11 Superconducting",
"claim": "Video inference speed 30 frames per second 60 million frames per second (2 million x improvement) Video inference energy (10 min video) 870,000 joules 192 Joules (4,500 x improvement)",
"quantified": true,
"figure": "60M fps vs 30 fps (2,000,000x); 192 J vs 870,000 J per 10-min video (4,500x)",
"baseline_stated": false,
"baseline": "generic: 'TODAY'S BEST AI HARDWARE' (column header), unnamed",
"workload_stated": true,
"workload": "video inference (10-minute video)",
"source_url": "https://www.greatsky.ai/tech",
"source_type": "company page",
"date": "2026",
"notes": "Tech-page table 'Transform the Numbers' ('All figures derived from experimental data and published research'); quoted as the row text renders (METRIC | TODAY'S BEST AI HARDWARE | OUR ARCHITECTURE). Absolute joule/fps figures with a stated task; the comparison column is an unnamed 'best AI hardware'. Bucket judgment: superconducting optoelectronic networks (Josephson-junction compute + single-photon optical links) filed under '11 Superconducting'; the taxonomy profile files Great Sky as photonic - flagged.",
"harvest": "2026-09-21"
},
{
"company": "Great Sky",
"bucket": "11 Superconducting",
"claim": "Superconductors and light operate at the physical limits of AI. The result is 300x reduction in cost of LLM tokens and a million x lower cost for video analysis.",
"quantified": false,
"figure": "300x LLM token cost; 1,000,000x video-analysis cost",
"baseline_stated": false,
"baseline": null,
"workload_stated": false,
"workload": null,
"source_url": "https://www.greatsky.ai/tech",
"source_type": "company page",
"date": "2026",
"notes": "Tech-page headline. Bare multipliers (cost), no unit/baseline/benchmark in the sentence; the table on the same page gives $0.01 vs $3 per 1M output tokens against the unnamed 'today's best' column.",
"harvest": "2026-09-21"
},
{
"company": "Great Sky",
"bucket": "11 Superconducting",
"claim": "This approach reduces communication energy by three orders of magnitude relative to conventional architectures and can sustain high-speed synaptic event rates at any network scale, a regime that is inaccessible to today's shared-router computing architectures.",
"quantified": false,
"figure": "1000x communication energy vs conventional architectures",
"baseline_stated": false,
"baseline": "generic: conventional architectures",
"workload_stated": false,
"workload": null,
"source_url": "https://www.greatsky.ai/news/genesis-mission",
"source_type": "company page",
"date": "2026-07",
"notes": "Genesis Mission selection post (Jul 22, 2026). Bare 'three orders of magnitude', generic baseline, no workload.",
"harvest": "2026-09-21"
},
{
"company": "Houmo.ai (后摩智能)",
"bucket": "0 Digital accelerator",
"claim": "鸿途H30 基于 SRAM 存储介质， 采用数字存算一体架构，拥有极低的访存功耗和超高的计算密度，在Int8数据精度条件下，其 AI 核心IPU 能效比高达15Tops/W，是传统架构芯片的7倍以上。",
"quantified": true,
"figure": "15 TOPS/W (IPU core, INT8); >7x vs traditional-architecture chips",
"baseline_stated": false,
"baseline": "generic: 传统架构芯片 (traditional-architecture chips)",
"workload_stated": false,
"workload": null,
"source_url": "https://m.leiphone.com/category/chips/fuoKI6lcumCrbeVE.html",
"source_type": "exec statement (launch coverage, Leiphone)",
"date": "2023-05",
"notes": "H30 launch (May 10, 2023), statement by co-founder/R&D VP 陈亮 as reported by Leiphone (IT之家 carries the same wording; the eet-china original blocks fetch). Gloss: 'H30 uses SRAM and a fully digital CIM architecture ... at INT8 its AI-core IPU reaches 15 TOPS/W, more than 7x traditional-architecture chips.' Absolute TOPS/W with a precision condition; multiplier against a generic class. DIGITAL SRAM compute-in-memory on TSMC 12nm (no analog, no emerging device) - coded '0 Digital accelerator' per instruction; the taxonomy files it under analog-imc - flagged.",
"harvest": "2026-09-21"
},
{
"company": "Houmo.ai (后摩智能)",
"bucket": "0 Digital accelerator",
"claim": "鸿途H30基于12nm工艺制程， 在Int8数据精度下实现高达256TOPS的物理算力，所需功耗不超过 35W，整个SoC能效比达到 7.3Tops/W，具有高计算效率、低计算延时、低工艺依赖等特点。",
"quantified": true,
"figure": "256 TOPS INT8 at <=35 W; 7.3 TOPS/W SoC-level",
"baseline_stated": false,
"baseline": null,
"workload_stated": false,
"workload": null,
"source_url": "https://m.leiphone.com/category/chips/fuoKI6lcumCrbeVE.html",
"source_type": "exec statement (launch coverage, Leiphone)",
"date": "2023-05",
"notes": "Statement by co-founder/product VP 信晓旭 at the H30 launch. Gloss: '12nm; 256 TOPS physical compute at INT8 within 35 W; 7.3 TOPS/W for the whole SoC.' Absolute figures, no comparison system or benchmark. Digital SRAM CIM - see bucket note.",
"harvest": "2026-09-21"
},
{
"company": "Houmo.ai (后摩智能)",
"bucket": "0 Digital accelerator",
"claim": "根据后摩智能给出的数据，在实际性能测试中，鸿途H30 基于Resnet 50 模型的 Benchmark，在 Batch Size 等于1 和 8 的条件下分别达到了 8700 帧/秒和 10300 帧/秒的性能。",
"quantified": true,
"figure": "8,700 fps (batch 1) / 10,300 fps (batch 8) on ResNet-50 (H30)",
"baseline_stated": false,
"baseline": null,
"workload_stated": true,
"workload": "ResNet-50 benchmark, batch size 1 and 8",
"source_url": "https://m.leiphone.com/category/chips/fuoKI6lcumCrbeVE.html",
"source_type": "company data (launch coverage, Leiphone)",
"date": "2023-05",
"notes": "Leiphone reporting figures 'given by Houmo' at the H30 launch. Gloss: 'per data provided by Houmo, in real performance tests H30 reached 8,700 fps and 10,300 fps on the ResNet-50 benchmark at batch size 1 and 8.' Absolute throughput with a named benchmark; no comparison system. Digital SRAM CIM - see bucket note.",
"harvest": "2026-09-21"
},
{
"company": "iHW (Intelligent HW)",
"bucket": "4 In-memory/CIM",
"claim": "iHW has secured ultra-low-power neuromorphic technology that can significantly improve both power consumption and performance-per-watt from data centers and cloud to on-device AI.",
"quantified": false,
"figure": null,
"baseline_stated": false,
"baseline": null,
"workload_stated": false,
"workload": null,
"source_url": "https://en.wowtale.net/2026/08/19/234780/",
"source_type": "exec quote (funding coverage)",
"date": "2026-08",
"notes": "CEO quote in Series A coverage (Aug 19, 2026). Unquantified. The Elec adds (journalist wording) that the company targets 'performance comparable to Nvidia and Hailo products' on 28-40nm - not quoted. Infertron is an analog eFlash compute-in-memory chip; pre-product.",
"harvest": "2026-09-21"
},
{
"company": "iniVation",
"bucket": "0 Digital accelerator",
"claim": "High speed (equivalent 10k+ fps) and very low latency (< 1 ms)",
"quantified": true,
"figure": "equivalent 10k+ fps; <1 ms latency (DVS)",
"baseline_stated": false,
"baseline": null,
"workload_stated": false,
"workload": null,
"source_url": "https://inivation.com/technology/",
"source_type": "company page",
"date": "2025-08",
"notes": "Technology page, DVS advantages list. Sensor spec with absolute units; no comparison system or task. Event-based vision SENSOR - coded '0' per instruction; now a SynSense Group company.",
"harvest": "2026-09-21"
},
{
"company": "Intrinsic Semiconductor Technologies",
"bucket": "4 In-memory/CIM",
"claim": "Its technology can read data 10x to 100x faster and write it 1000x faster than existing solutions and is fabricated using standard processing techniques at the back-end-of-line, making it less complex and less expensive than other ReRAM solutions.",
"quantified": false,
"figure": "10-100x read, 1000x write vs existing solutions",
"baseline_stated": false,
"baseline": "generic: existing (embedded flash) solutions",
"workload_stated": false,
"workload": null,
"source_url": "https://www.sure-core.com/2023/11/01/surecore-and-intrinsic-announce-collaboration-to-accelerate-time-to-market-for-innovative-reram-technology/",
"source_type": "press release (joint, sureCore/Intrinsic)",
"date": "2023-11",
"notes": "Joint sureCore-Intrinsic PR (Nov 1, 2023), Intrinsic boilerplate. Memory-IP claim (SiOx ReRAM eNVM), not an AI-compute claim; bare multipliers, generic baseline.",
"harvest": "2026-09-21"
},
{
"company": "Intrinsic Semiconductor Technologies",
"bucket": "4 In-memory/CIM",
"claim": "Direct integration enabled by our silicon oxide devices reduces power consumption by orders of magnitude compared to external Flash",
"quantified": false,
"figure": "orders of magnitude vs external Flash",
"baseline_stated": false,
"baseline": "generic: external Flash",
"workload_stated": false,
"workload": null,
"source_url": "https://www.intrinsicsemi.com/",
"source_type": "company page",
"date": "2026",
"notes": "Homepage. Unquantified 'orders of magnitude'; memory-IP claim.",
"harvest": "2026-09-21"
},
{
"company": "Lynxi Technology (灵汐科技)",
"bucket": "0 Digital accelerator",
"claim": "能效比 ( 突触操作数 / 瓦 )：≥ 1TSops/W 扩展神经元数目：200万 @ 稀疏模式 扩展突触数目：20亿 @ 稀疏模式 内置视频和图片编解码器：480FPS@1080P 算力指标：16TFlops@FP16，32Tops@INT8",
"quantified": true,
"figure": ">=1 TSOPS/W (synaptic ops/W); 16 TFLOPS FP16; 32 TOPS INT8 (KA200)",
"baseline_stated": false,
"baseline": null,
"workload_stated": false,
"workload": null,
"source_url": "https://cnsubsites.chinadaily.com.cn/wic/2021-11/11/c_169652.htm",
"source_type": "award nomination (company-submitted spec, WIC 2021)",
"date": "2021-11",
"notes": "KA200 technical-indicator block from the World Internet Conference 2021 'Leading Scientific and Technological Achievements' nomination page (company-submitted material, hosted by China Daily; lynxi.com itself would not serve). Gloss: 'energy efficiency (synaptic ops per watt) >= 1 TSOPS/W ... compute: 16 TFLOPS@FP16, 32 TOPS@INT8.' Absolute unit figures; no comparison or benchmark. Digital many-core brain-inspired chip (Tianjic lineage) - coded '0'.",
"harvest": "2026-09-21"
},
{
"company": "MemryX Inc.",
"bucket": "0 Digital accelerator",
"claim": "MemryX is currently in production with its MX3 silicon, delivering >20× better performance per watt than mainstream GPUs for targeted AI inference applications.",
"quantified": false,
"figure": ">20x perf/W vs mainstream GPUs",
"baseline_stated": false,
"baseline": "generic: mainstream GPUs",
"workload_stated": false,
"workload": null,
"source_url": "https://www.prnewswire.com/news-releases/memryx-unveils-mx4-roadmap-enabling-distributed-asynchronous-dataflow-for-highly-efficient-data-center-ai-302649698.html",
"source_type": "press release",
"date": "2025-12",
"notes": "MX4 roadmap PR (Dec 26, 2025). Bare multiplier, generic GPU class, 'targeted' applications unnamed. Digital at-memory dataflow (bfloat16/INT MACs, no analog or emerging device despite the Wei Lu lineage) - coded '0'.",
"harvest": "2026-09-21"
},
{
"company": "MemryX Inc.",
"bucket": "0 Digital accelerator",
"claim": "The most power-efficient AI accelerators for inference — from a single chip to the data center.",
"quantified": false,
"figure": null,
"baseline_stated": false,
"baseline": null,
"workload_stated": false,
"workload": null,
"source_url": "https://memryx.com/",
"source_type": "company page",
"date": "2026",
"notes": "Homepage footer tagline. Pure superlative.",
"harvest": "2026-09-21"
},
{
"company": "Mentium Technologies",
"bucket": "4 In-memory/CIM",
"claim": "Built on a unique and proprietary architecture, Mentium's accelerators provide complex endpoint AI performance below 0.5 W, enabling real‑time inference in size‑, weight‑, and power‑constrained systems without requiring customers to redesign their existing hardware.",
"quantified": true,
"figure": "<0.5 W endpoint AI",
"baseline_stated": false,
"baseline": null,
"workload_stated": false,
"workload": null,
"source_url": "https://mentium.tech/",
"source_type": "company page",
"date": "2026",
"notes": "Homepage. Absolute power bound; no comparison or task. Hybrid digital + analog in-memory co-processor (Strukov/Likharev lineage; device not disclosed).",
"harvest": "2026-09-21"
},
{
"company": "Mentium Technologies",
"bucket": "4 In-memory/CIM",
"claim": "Luna-1 Extreme-low power AI solution for IoT 2.4 TOPS 70mW typical power xSPI, GPIO interfaces",
"quantified": true,
"figure": "2.4 TOPS at 70 mW typical (Luna-1); Luna-2 listed at 4.2 TOPS / 130 mW",
"baseline_stated": false,
"baseline": null,
"workload_stated": false,
"workload": null,
"source_url": "https://mentium.tech/",
"source_type": "company page",
"date": "2026",
"notes": "Product tile as rendered on the homepage. Absolute TOPS and mW figures (implies ~34 TOPS/W) with no stated precision, baseline or benchmark.",
"harvest": "2026-09-21"
},
{
"company": "Neuromorphyx",
"bucket": "0 Digital accelerator",
"claim": "Sub-1 W complete-stack power",
"quantified": true,
"figure": "<1 W complete stack (BrainBoard1500 / NeuroCore)",
"baseline_stated": false,
"baseline": null,
"workload_stated": false,
"workload": null,
"source_url": "https://neuromorphyx.com/",
"source_type": "company page",
"date": "2026",
"notes": "Homepage feature line (retrieved via page-reader; site blocks direct fetch). Absolute power bound, no baseline/task. Neuromorphyx is a board integrator around BrainChip's digital AKD1500 - coded '0'; the underlying efficiency claims are BrainChip's (already in claims.json).",
"harvest": "2026-09-21"
},
{
"company": "Neuronova",
"bucket": "5 Neuromorphic/SNN",
"claim": "Our networks achieve the same accuracy while consuming up to 1000x less power and usingonly a few hundred parameters.",
"quantified": false,
"figure": "up to 1000x less power (vs digital ANN on conventional hardware)",
"baseline_stated": false,
"baseline": "generic: artificial neural networks on conventional digital hardware",
"workload_stated": true,
"workload": "keyword recognition ('Hey Neuronova!')",
"source_url": "https://www.neuronovatech.com/",
"source_type": "company page",
"date": "2026",
"notes": "Homepage ('usingonly' sic). Task named, but bare multiplier against a generic class. H1 is a fully analog neuromorphic processor, 180nm, silicon-validated, pre-product.",
"harvest": "2026-09-21"
},
{
"company": "Neuronova",
"bucket": "5 Neuromorphic/SNN",
"claim": "In digital-equivalent terms, this corresponds to 145 TOPS/W efficiency for temporal inference.",
"quantified": true,
"figure": "145 TOPS/W digital-equivalent; <1 uW always-on inference",
"baseline_stated": false,
"baseline": null,
"workload_stated": false,
"workload": null,
"source_url": "https://www.neuronovatech.com/",
"source_type": "company page",
"date": "2026",
"notes": "Homepage. Absolute unit figure, but explicitly a 'digital-equivalent' conversion for analog/time-domain computation; no baseline or benchmark. Spec tile on the same page: '<1μW Always-on inference power'.",
"harvest": "2026-09-21"
},
{
"company": "Neuronova",
"bucket": "5 Neuromorphic/SNN",
"claim": "We provide a chip that reduces power consumption by a factor of 1,000, enabling the device to perform these tasks locally while consuming far less energy.",
"quantified": false,
"figure": "1000x power reduction",
"baseline_stated": false,
"baseline": null,
"workload_stated": false,
"workload": null,
"source_url": "https://tech.eu/2025/01/20/bringing-the-brain-to-silicon-neuronovas-vision-for-energy-efficient-ai-hardware/",
"source_type": "exec quote (interview)",
"date": "2025-01",
"notes": "Alessandro Milozzi (CEO) in tech.eu interview. Bare multiplier.",
"harvest": "2026-09-21"
},
{
"company": "Neurophos",
"bucket": "6 Photonic",
"claim": "ENERGY EFFICIENCY TOPS/W T100 OPU 235 TOPS/W NVIDIA B200 7 TOPS/W 34× ADVANTAGE",
"quantified": true,
"figure": "235 TOPS/W (T100 OPU) vs 7 TOPS/W (NVIDIA B200); 34x",
"baseline_stated": true,
"baseline": "NVIDIA B200 (datasheet values)",
"workload_stated": true,
"workload": "sustained prefill inference at INT8, 'measured on public workloads' (workload not individually named)",
"source_url": "https://www.neurophos.com/",
"source_type": "company page",
"date": "2026",
"notes": "Homepage 'HEAD TO HEAD' comparison table (quoted as rendered), with the stated condition 'Sustained prefill inference at INT8. Measured on public workloads. NVIDIA B200 datasheet values.' Named baseline and stated condition, so quantified; workload coded yes as a named task class (LLM prefill) though no specific model/benchmark is listed - borderline. NOTE: T100 OPU 'ships in 2028' - these are projected figures for unreleased silicon; the same page elsewhere shows '~300 TOPS/W' and '40x TOPS/W vs GPU'.",
"harvest": "2026-09-21"
},
{
"company": "Neurophos",
"bucket": "6 Photonic",
"claim": "This innovation delivers up to 100x the performance and energy efficiency of current leading chips, offering a practical, drop-in replacement for GPUs in data centers.",
"quantified": false,
"figure": "up to 100x performance and energy efficiency vs current leading chips",
"baseline_stated": false,
"baseline": "generic: current leading chips",
"workload_stated": false,
"workload": null,
"source_url": "https://www.prnewswire.com/news-releases/neurophos-secures-110-million-series-a-to-launch-exaflop-scale-photonic-ai-chips-302667517.html",
"source_type": "press release",
"date": "2026-01",
"notes": "$110M Series A PR (Jan 2026). Bare multiplier, generic baseline.",
"harvest": "2026-09-21"
},
{
"company": "OLIX Computing",
"bucket": "0 Digital accelerator",
"claim": "DX-1 can achieve the pareto optimal inference performance delivering simultaneously over 10,000 Tokens per second per user at higher output token throughput per watt than general purpose chips running large batch sizes.",
"quantified": true,
"figure": ">10,000 tokens/s per user; higher tokens/s/W than general-purpose chips",
"baseline_stated": false,
"baseline": "generic: general purpose chips running large batch sizes",
"workload_stated": false,
"workload": null,
"source_url": "https://olix.com/news/company-raises-series-b",
"source_type": "press release",
"date": "2026-08",
"notes": "Series B post (Aug 3, 2026). Absolute throughput figure (tokens/s per user) but no model named and the efficiency comparison is a generic 'higher'. BUCKET JUDGMENT: OLIX's OTPU is an 'optical digital processor' - digital SRAM-resident compute with a photonic ('slow and wide') interconnect, not optical matrix multiplication; coded '0 Digital accelerator' per the purely-digital-CMOS rule, while the taxonomy files it as photonic - flagged for the substrate chart.",
"harvest": "2026-09-21"
},
{
"company": "POLYN Technology",
"bucket": "5 Neuromorphic/SNN",
"claim": "Ultra-low power: approximately 34 µW during continuous operation",
"quantified": true,
"figure": "~34 uW continuous (NASP VAD core); 50 us per inference",
"baseline_stated": false,
"baseline": null,
"workload_stated": true,
"workload": "voice activity detection (VAD) core, always-on",
"source_url": "https://polyn.ai/polyn-technology-announces-first-silicon-implemented-nasp-chip/",
"source_type": "press release",
"date": "2025-10",
"notes": "First-silicon NASP chip PR (Oct 28, 2025); companion line 'Ultra-low latency: 50 microseconds per inference'. Absolute figures for a named task; no comparison system. Fixed-function analog neural circuits (resistor networks + op-amps) on mature CMOS - bucket '5' is a judgment call (analog neuromorphic, not in-memory). Wording retrieved via page-reader (polyn.ai returns 403 to direct fetch); identical figures in embedded.com and EDN coverage.",
"harvest": "2026-09-21"
},
{
"company": "POLYN Technology",
"bucket": "5 Neuromorphic/SNN",
"claim": "The NASP platform employs trained neural networks in the analog domain to perform AI inference with much lower power consumption than conventional digital neural processors.",
"quantified": false,
"figure": null,
"baseline_stated": false,
"baseline": "generic: conventional digital neural processors",
"workload_stated": false,
"workload": null,
"source_url": "https://polyn.ai/polyn-technology-announces-first-silicon-implemented-nasp-chip/",
"source_type": "press release",
"date": "2025-10",
"notes": "Same PR. Unquantified superlative. Retrieved via page-reader.",
"harvest": "2026-09-21"
},
{
"company": "Prophesee",
"bucket": "0 Digital accelerator",
"claim": "ULTRA-LOW POWER Down to 36 μW at sensor level The Metavision sensor's pixel independence and intelligent power modes architecture enable new levels of power efficiency starting at just 36μW in ultra-low power and 3mW typical operating power.",
"quantified": true,
"figure": "36 uW sensor-level ultra-low-power mode; 3 mW typical (GenX320)",
"baseline_stated": false,
"baseline": null,
"workload_stated": false,
"workload": null,
"source_url": "https://www.prophesee.ai/event-based-sensor-genx320/",
"source_type": "company page",
"date": "2023-10",
"notes": "GenX320 product page (launched Oct 16, 2023). Absolute power figure for a specific mode; event-based vision SENSOR - coded '0' per instruction.",
"harvest": "2026-09-21"
},
{
"company": "Prophesee",
"bucket": "0 Digital accelerator",
"claim": "With each pixel only reporting when it senses movement, Metavision sensors generate on average 10 to 1000x less data than traditional image-based ones.",
"quantified": false,
"figure": "10-1000x less data vs frame-based sensors",
"baseline_stated": false,
"baseline": "generic: traditional image-based sensors",
"workload_stated": false,
"workload": null,
"source_url": "https://www.prophesee.ai/event-based-sensor-genx320/",
"source_type": "company page",
"date": "2023-10",
"notes": "Same page. Bare multiplier (data volume), generic baseline; the launch PR headline calls GenX320 'the world's smallest and most power-efficient event-based vision sensor'.",
"harvest": "2026-09-21"
},
{
"company": "Reexen Technology (九天睿芯)",
"bucket": "4 In-memory/CIM",
"claim": "The chip came into production in 2021 and provides a performance of 20TOPS/W while consuming 10 to 100microwatts, the company states.",
"quantified": true,
"figure": "20 TOPS/W at 10-100 uW (ADA100)",
"baseline_stated": false,
"baseline": null,
"workload_stated": false,
"workload": null,
"source_url": "https://www.eenewseurope.com/en/neuromorphic-startup-reexen-gains-ground-with-goertek/",
"source_type": "company statement (quoted in coverage)",
"date": "2022-03",
"notes": "eeNews Europe (Mar 28, 2022) reporting company-supplied figures ('the company states'); the same article quotes a spokesperson giving 20 uW for VAD and 160 uW for KWS in Goertek's smart mic. Absolute unit figures; no comparison or benchmark named in the sentence. Analog/mixed-signal CIM (ETH INI lineage).",
"harvest": "2026-09-21"
},
{
"company": "Reexen Technology (九天睿芯)",
"bucket": "4 In-memory/CIM",
"claim": "The ADA300 – targeting TSMC's 6nm process – will provide 24TOPS at 55 TOPS/W.",
"quantified": true,
"figure": "24 TOPS at 55 TOPS/W (ADA300, roadmap)",
"baseline_stated": false,
"baseline": null,
"workload_stated": false,
"workload": null,
"source_url": "https://www.eenewseurope.com/en/neuromorphic-startup-reexen-gains-ground-with-goertek/",
"source_type": "company roadmap (quoted in coverage)",
"date": "2022-03",
"notes": "Roadmap figure for an unreleased chip (ADA300; ADA200 at 2-4 TOPS / 20 TOPS/W in the same passage). Absolute units, forward-looking. Reexen's current site (2026) repositions ADA300 as an SRAM-CIM + HBF stacked inference part.",
"harvest": "2026-09-21"
},
{
"company": "Weebit Nano",
"bucket": "4 In-memory/CIM",
"claim": "Measured energy consumption: 180 pJ/syn. ev. (3 pJ at synaptic level)",
"quantified": true,
"figure": "180 pJ per synaptic event (system); 3 pJ at synaptic level",
"baseline_stated": false,
"baseline": null,
"workload_stated": true,
"workload": "MNIST digit recognition, single-layer spiking perceptron (proof-of-concept SNN with CEA-Leti, 130nm)",
"source_url": "https://www.weebit-nano.com/wp-content/uploads/2024/09/SOCC241.pdf",
"source_type": "company presentation (SOCC 2024 slides)",
"date": "2024-09",
"notes": "Weebit SOCC 2024 deck, slide 'Fully Integrated Spiking Neural Network using Weebit ReRAM as a Synaptic Device' (A. Regev, AICAS 2020, Weebit/Leti): 82% accuracy vs 88% theoretical max, 11.5k RRAM, 1.8 mm2. Absolute energy figure with a named task; no comparison system. Weebit's business is ReRAM eNVM IP; neuromorphic is R&D-only - flagged.",
"harvest": "2026-09-21"
},
{
"company": "Weebit Nano",
"bucket": "4 In-memory/CIM",
"claim": "Data is stored locally on the chip and not in the cloud so there is greater security, much faster reaction and lower power consumption",
"quantified": false,
"figure": null,
"baseline_stated": false,
"baseline": "generic: cloud-based processing",
"workload_stated": false,
"workload": null,
"source_url": "https://www.weebit-nano.com/a-complete-no-brainerreram-for-neuromorphic-computing/",
"source_type": "company page",
"date": "2024-06",
"notes": "Company blog 'ReRAM for Neuromorphic Computing' (Jun 5, 2024). Unquantified.",
"harvest": "2026-09-21"
},
{
"company": "Yizhu Technology (亿铸科技)",
"bucket": "4 In-memory/CIM",
"claim": "目前，亿铸科技点亮了基于忆阻器的高精度、低功耗存算一体AI大算力POC芯片，基于传统工艺制程，能效比表现经第三方机构验证，超出传统架构AI芯片平均性能的10倍以上。",
"quantified": false,
"figure": ">10x energy efficiency vs traditional-architecture AI chips (third-party verified, unnamed)",
"baseline_stated": false,
"baseline": "generic: 传统架构AI芯片 average (traditional-architecture AI chips)",
"workload_stated": false,
"workload": null,
"source_url": "https://www.yizhu-tech.com/about.php",
"source_type": "company page",
"date": "2026",
"notes": "About page. Gloss: 'Yizhu has powered up a memristor(ReRAM)-based high-precision, low-power CIM large-compute POC chip on a mature process; its energy-efficiency ratio, verified by a third-party body, exceeds the average of traditional-architecture AI chips by more than 10x.' Bare multiplier, verifier unnamed. All-digital ReRAM CIM (in-array compute on an emerging device) - coded '4'.",
"harvest": "2026-09-21"
},
{
"company": "Yizhu Technology (亿铸科技)",
"bucket": "4 In-memory/CIM",
"claim": "同在75W功耗的前提下，亿铸ReRAM存算一体大算力板卡算力可达1POPS（INT8）以上",
"quantified": true,
"figure": ">1 POPS INT8 at 75 W (accelerator card, projected)",
"baseline_stated": false,
"baseline": null,
"workload_stated": false,
"workload": null,
"source_url": "https://yizhu-tech.com/newsv.php?id=30",
"source_type": "company page (reposted CTO interview)",
"date": "2022-09",
"notes": "CTO interview reposted on yizhu-tech.com (Sept 2, 2022). Gloss: 'at the same 75 W, Yizhu's ReRAM CIM large-compute card can reach over 1 POPS (INT8).' Absolute figure with a stated power envelope, forward-looking; the same piece says the second-generation card 'may reach about 10x the energy efficiency of current mainstream AI accelerator cards'. Retrieved via page-reader (site returns navigation-only to direct fetch).",
"harvest": "2026-09-21"
},
{
"company": "CyberSwarm",
"bucket": "4 In-memory/CIM",
"claim": "This means no more running back and forth, leading to faster and more energy-efficient computers.",
"quantified": false,
"figure": null,
"baseline_stated": false,
"baseline": "generic: von Neumann computers",
"workload_stated": false,
"workload": null,
"source_url": "https://www.cyber-swarm.net/technology/",
"source_type": "company page",
"date": "2026",
"notes": "Technology page (memristor 'Neuromorphic Crossbar' SoC). Pure superlative; no figure anywhere on the site. No taxonomy profile exists.",
"harvest": "2026-09-21"
},
{
"company": "Tsavorite Scalable Intelligence",
"bucket": "0 Digital accelerator",
"claim": "Unified software orchestration and distributed coherence allow seamless transitions between fine-tuning, training, and large-scale inference; accelerating workloads while reducing cost and power by up to 90%.",
"quantified": false,
"figure": "up to 90% cost and power reduction",
"baseline_stated": false,
"baseline": null,
"workload_stated": false,
"workload": null,
"source_url": "https://tsavoritesi.com/",
"source_type": "company page",
"date": "2026",
"notes": "Homepage. Bare percentage with no baseline system or benchmark. NOTE: the target named 'Tsavorite Technologies' resolves to Tsavorite Scalable Intelligence, Inc. (Milpitas/Bangalore, founded 2023): a chiplet-based digital 'Omni Processing Unit' on Arm Neoverse + Samsung SF4X, $100M pre-orders (Nov 2025) - purely digital, coded '0'. No taxonomy profile.",
"harvest": "2026-09-21"
},
{
"company": "Tsavorite Scalable Intelligence",
"bucket": "0 Digital accelerator",
"claim": "10x increase in arithmetic intensity The MultiPlexus fabric keeps data closer to compute, reducing redundant transfers and increasing arithmetic intensity tenfold.",
"quantified": false,
"figure": "10x arithmetic intensity",
"baseline_stated": false,
"baseline": null,
"workload_stated": false,
"workload": null,
"source_url": "https://tsavoritesi.com/",
"source_type": "company page",
"date": "2026",
"notes": "Homepage. Bare multiplier.",
"harvest": "2026-09-21"
},
{
"company": "Mastiska AI",
"bucket": "0 Digital accelerator",
"claim": "Training and Inferencing of Transformer (and future neural architecture) based foundation models, at 50-100x more energy efficiently compared to today's GPU-based solutions.",
"quantified": false,
"figure": "50-100x energy efficiency vs GPU-based solutions",
"baseline_stated": false,
"baseline": "generic: today's GPU-based solutions",
"workload_stated": false,
"workload": null,
"source_url": "https://semiwiki.com/ceo-interviews/338703-ceo-interview-suresh-sugumar-of-mastiska-ai/",
"source_type": "exec quote (interview)",
"date": "2023-12",
"notes": "Suresh Sugumar (CEO) in SemiWiki CEO interview (Dec 9, 2023). Bare multiplier, generic baseline, workload is a model class not a benchmark. Self-described 'brain-like' / neuromorphic-technique computer; initial revenue from FPGA-based servers (Dubai; $10M raised) - implementation is digital, coded '0' and flagged. No taxonomy profile; mastiska.ai served no content.",
"harvest": "2026-09-21"
},
{
"company": "Extropic",
"bucket": "1 Thermodynamic",
"claim": "Introducing Z1T, our first family of transformer-like models for sparse probabilistic chips like Z1, exhibiting over 100x energy efficiency gains vs GPUs, and featuring a corresponding scaling law",
"quantified": false,
"figure": ">100x energy efficiency vs GPUs (modeled decode pass, Z1 + FPGA vs GPU)",
"baseline_stated": false,
"baseline": "generic: GPUs (the body text compares the decode pass to an H100 GPU, but the headline claim is 'vs GPUs')",
"workload_stated": true,
"workload": "Z1T sparse transformer decode pass at GPT-2-small-equivalent validation loss (OpenWebText, GPT-2 BPE)",
"source_url": "https://extropic.ai/writing/z1t",
"source_type": "company page",
"date": "2026-09-04",
"notes": "Company research post announcing Z1T with open weights. Energy figure is a modeled/estimated comparison of a disaggregated Z1+FPGA decode pipeline against a GPU, not a measurement on Z1 silicon (Z1 was only taped out per the same-day announcement). Bare multiplier against a generic GPU class -> unquantified under the README rule.",
"harvest": "2026-09-21-news"
},
{
"company": "Extropic",
"bucket": "1 Thermodynamic",
"claim": "As we will see in later sections, the operations done on Z1 have about three orders of magnitude energetic efficiency gain compared to GPUs, and as such, Z1T ends up with a two orders of magnitude energy efficiency gain.",
"quantified": false,
"figure": "~1000x per-operation energy vs GPUs; ~100x model-level after ~10x FLOP overhead",
"baseline_stated": false,
"baseline": "generic: GPUs",
"workload_stated": true,
"workload": "Z1T sparse transformer matching GPT-2 loss (needs ~9.5e19 FLOPs to match GPT-2-small)",
"source_url": "https://extropic.ai/writing/z1t",
"source_type": "company page",
"date": "2026-09-04",
"notes": "Same post; explains the 100x headline as 1000x per-op gain divided by a ~10x FLOP penalty from sparsity. Order-of-magnitude phrasing with no unit and no named GPU -> unquantified.",
"harvest": "2026-09-21-news"
},
{
"company": "Extropic",
"bucket": "1 Thermodynamic",
"claim": "Z1 chip 269,000 pbits in a sparse graph, 16-neighbor connectivity >50 MHz Sampling rate <1 W Power consumption",
"quantified": true,
"figure": "269,000 pbits; >50 MHz sampling rate; <1 W chip power",
"baseline_stated": false,
"baseline": "none (absolute spec)",
"workload_stated": false,
"workload": "none stated (chip-level spec sheet)",
"source_url": "https://extropic.ai/writing/from-one-to-one-billion/",
"source_type": "company page",
"date": "2026-09-04",
"notes": "'Z1 systems summary' table in the 'From One to One Billion' announcement (page header says Summer 2026; Dealroom and Extropic's X post date it 2026-09-04/05). Spec for a taped-out chip with early access in 2027, so the figures are design targets, not measurements. Counts as quantified because W and MHz are unit figures. Same post also lists M.2 stick '500,000+' pbits, PCIe card '4,000,000+' pbits, cluster '1,000,000,000+' pbits.",
"harvest": "2026-09-21-news"
},
{
"company": "NVIDIA",
"bucket": "0 Incumbent GPU/TPU",
"claim": "New SemiAnalysis AgentX data measured using real-world agentic coding trajectories shows Vera Rubin NVL72 systems deliver 30x higher throughput per megawatt and 45x lower token costs than NVIDIA GB300 NVL72.",
"quantified": true,
"figure": "30x throughput per MW; 45x lower cost per M tokens vs GB300 NVL72",
"baseline_stated": true,
"baseline": "NVIDIA GB300 NVL72",
"workload_stated": true,
"workload": "SemiAnalysis AgentX agentic-coding trajectories, DeepSeek V4 Pro model",
"source_url": "https://blogs.nvidia.com/blog/vera-rubin-nvl72-efficiency-ai-agents/",
"source_type": "company blog",
"date": "2026-08-24",
"notes": "Blog subtitle; body: 'as much as 30x higher throughput per megawatt than GB300 NVL72 on the DeepSeek V4 Pro model' and 'These early results ... don't yet reflect Vera CPU performance for tool calling.' Updated 2026-09-15. Included as the incumbent's flagship efficiency claim for comparison; fully specified (x-factor + named baseline + named workload).",
"harvest": "2026-09-21-news"
},
{
"company": "NVIDIA",
"bucket": "0 Incumbent GPU/TPU",
"claim": "In its first MLPerf Inference preview submission, the NVIDIA Vera Rubin NVL72 system delivered up to 3.7x higher throughput than NVIDIA GB300 NVL72, demonstrating the performance gains possible with NVIDIA's next-generation AI infrastructure.",
"quantified": true,
"figure": "up to 3.7x throughput vs GB300 NVL72",
"baseline_stated": true,
"baseline": "NVIDIA GB300 NVL72",
"workload_stated": true,
"workload": "MLPerf Inference v6.1 (preview category)",
"source_url": "https://blogs.nvidia.com/blog/ai-infra-summit-vera-rubin-dsx-energy-efficiencies-tokens-per-watt-ai-factories/",
"source_type": "company blog",
"date": "2026-09-16",
"notes": "Throughput, not per-watt; the same live blog claims DSX MaxLPS delivers 'up to 1.4x more tokens per megawatt' (bare multiplier, no baseline) and Lambda 'improved performance per watt by 23%'. Peer-reviewed MLPerf benchmark named.",
"harvest": "2026-09-21-news"
},
{
"company": "Euclyd",
"bucket": "0 Digital accelerator",
"claim": "At the center of EUCLYD's roadmap are craftwerk, the world's first agentic AI silicon, and craftwerk station CWS, the world's lowest-power exascale AI factory.",
"quantified": false,
"figure": "none (superlative: 'world's lowest-power exascale AI factory')",
"baseline_stated": false,
"baseline": "none",
"workload_stated": false,
"workload": "none",
"source_url": "https://www.euclyd.ai/press-release",
"source_type": "press release",
"date": "2026-09-15",
"notes": "Series A (> EUR 200M, co-led by Samsung) press release for a company with no shipped silicon; Korea Economic Daily coverage (via bloomingbit) attributes '1 terabyte of UBM' and 'as much as 8,000 terabytes per second of memory bandwidth' to Euclyd's Craftwork, but those figures are reported speech, not on the company's own page, so they are not coded here. New company, not in claims.json. | Re-bucketed 0 Digital accelerator on merge (near-memory 3D-DRAM digital co-design, not an analog substrate).",
"harvest": "2026-09-21-news"
}
]
}
}