The Index
We're building a thermodynamic neural processor in the open, one chapter at a time. Read straight through from Chapter 1, or expand any chapter below for the short version and jump in where you like. The reference pages stand on their own.
Chapters
Chapter 1Transforms will be Assimilated
Ship synapses. Stream transforms. Forget weights.
Transforms will be Assimilated
Ship synapses. Stream transforms. Forget weights.
The thesis for the whole series: the unit of intelligence worth shipping isn't the weight, it's the transform. Today's AI burns enormous energy computing weights and hauling them through memory hierarchies, but weights are a means, not the thing that matters. A synapse isn't a number — it's a physical adaptive element that unites memory and processing, and ASSIMILAATE is the plan to ship that.
Chapter 2Before I Knew of the Word Memristor
Twenty-five years assembling the synaptic solution.
Before I Knew of the Word Memristor
Twenty-five years assembling the synaptic solution.
The origin story. How a frustrated undergraduate physics major wandered into neural networks, concluded that separating memory from processing was the central barrier to AI, and went looking for a way to build a synapse — years before he'd heard the word memristor. The first Knowm patents come out of this, and the thread runs straight to everything that follows.
Chapter 3The Thermodynamic Bit
Nature builds with one primitive, at every scale, and you've stared at it your whole life without actually seeing it.
The Thermodynamic Bit
Nature builds with one primitive, at every scale, and you've stared at it your whole life without actually seeing it.
The builder block you've stared at your whole life without seeing. Across wildly different scales, Nature organizes matter the same way: free energy dissipating through adaptive pathways that compete for the flow. This chapter names that pattern the thermodynamic bit (kT-bit) and sets it down as a computational primitive we can bring into electronics.
Chapter 3bThe Thermodynamic Bit, Derived
Store a weight in two memristors instead of N bits, and optimal annealing, Bayesian inference, and native probabilistic sampling become a property of matter.
The Thermodynamic Bit, Derived
Store a weight in two memristors instead of N bits, and optimal annealing, Bayesian inference, and native probabilistic sampling become a property of matter.
The kT-bit, derived. Where Chapter 3 names the pattern, this one builds it from two memristors: how a differential pair plays the role of a synapse, why the pinched hysteresis loop is what defines a memristor, and how wiring two of them as a voltage divider gives you a weight that holds more than any single number can.
Chapter 3cThe Thermodynamic Bit, Built
Simulate a memristor synapse, draw it polygon by polygon, and send it to the fab — the part of building neuromorphic hardware where a circuit idea becomes silicon.
The Thermodynamic Bit, Built
Simulate a memristor synapse, draw it polygon by polygon, and send it to the fab — the part of building neuromorphic hardware where a circuit idea becomes silicon.
The kT-bit, built. The journey of one Knowm synapse from a circuit you simulate on a laptop, to a layout drawn by hand polygon by polygon in Electric, to a real device you can probe on the bench. The unglamorous reality before any fab gets involved: somebody has to draw every electrode and via.
Chapter 4The Neural Lane
kT-RAM is the genus. The neural lane is a species.
The Neural Lane
kT-RAM is the genus. The neural lane is a species.
The genus gets its first species. kT-RAM is just addressable kT-bits plus an instruction set, where reading the memory is computing with it; the neural lane is one way to build that — shrink a crossbar until the sneak paths can't hurt you, use one memristor at a time, and stack the synapses as voltage dividers onto a shared output line. Which synapses fire is set by an Activation Address Tuple: an ordered list of integers that is the wiring pattern itself, with no routing table in between. Along the way, why communicating continuous values across a neural network was always a fantasy, and why an RGB pixel was an AAT all along.
Chapter 4bThe Neural Lane Emulator
An open Python emulator of the 2-1 neural lane — its object model and instruction set — checked on a single synapse against the Chapter 3b physics: inertia, evidence-counting, and the read as a thermal sample.
The Neural Lane Emulator
An open Python emulator of the 2-1 neural lane — its object model and instruction set — checked on a single synapse against the Chapter 3b physics: inertia, evidence-counting, and the read as a thermal sample.
The code arrives. kt-ram-neural-core is an open Python emulator of the 2-1 neural lane, its object model reading like the hardware from the outside in — Core, NeuralLane, UnitCrossbarPair, addressed with AATs the way the real thing is. Collapse it to a single differential pair and you drive the whole fourteen-instruction set by hand: four reads, ten feedbacks, two letters each. On that one synapse the Chapter 3b physics comes right back out — the magnitude as inertia and as a running count of evidence, the read as a noisy thermal sample, and the writable coin P(+) = Φ(w/σ). One pip install, one Colab notebook, start poking.
Reference
ReferenceThe kT-bit Catalog
Every place in Nature the thermodynamic bit actually shows up — and the ones that only look like it.
The kT-bit Catalog
Every place in Nature the thermodynamic bit actually shows up — and the ones that only look like it.
A companion to Chapter 3: every place in Nature the thermodynamic bit actually shows up — rivers, vortices, organelles, even markets — each scored against three gates that decide whether it's really a kT-bit, and a fourth that tells you where to look. A working filter, held loosely, with confidence scores where the fit wobbles.
ReferenceThe kT-bit Reading List
The thinkers — a chemist, a physicist, a systems theorist, an ecologist, a roboticist — who circled the same primitive the kT-bit names, and where to find their work.
The kT-bit Reading List
The thinkers — a chemist, a physicist, a systems theorist, an ecologist, a roboticist — who circled the same primitive the kT-bit names, and where to find their work.
The cross-disciplinary lineage behind the kT-bit. A chemist, a physicist, a systems theorist, an ecologist, a roboticist — thinkers who each described one face of the same primitive, energy self-organizing matter as it dissipates, without quite saying it was one thing. Where to find their work, and how each piece connects.
ReferenceA Decade of Knowm SDC Memristor Literature, Part 1
What the research community did with Knowm's Self-Directed Channel memristor across a decade of papers.
A Decade of Knowm SDC Memristor Literature, Part 1
What the research community did with Knowm's Self-Directed Channel memristor across a decade of papers.
Tim's survey of what the world did with Knowm's memristor. The Self-Directed Channel device has been on sale since 2016, and over a few weeks Tim built a corpus of every paper he could find that uses or cites it — or the MSS model — and ran it through a reproducible mining pipeline. Out came 337 papers: who put a real device on the bench, who extended the model, and where the architecture has barely traveled yet. It's meant as a map for anyone about to work with these devices, generated from a single papers.json you can download and query yourself.
Automated reader? The machine-readable index is at /llms.txt.