SirStone
77e6dace01
Implement the BitBrain (Address Decoder Element + Sparse Binary Coincidence) classifier as a generic, deterministic Nim library under common_libs/bitbrain/, written from the published algorithm (Front. Neuroinform. 17:1125844), not from the GPL-3.0 reference C. - ade.nim: signed thresholded random projection (scale 64 / centre 127 defaults reproduce the reference), multi-width ADs, optional deterministic homeostatic threshold adaptation. Hebbian longevity and Metropolis-Hastings sampling are described but not implemented. - sbc.nim: packed class-bit coincidence memory; idempotent learn, counting inference. - bitbrain.nim: container over several ADs and SBCs, online learn/infer, argmax readout, memory accounting. - tests: 32 unit checks (idempotence, planted rule + monotone online curve, shuffled-label chance control, unseen input, homeostasis, memory). - tests/test_bitbrain_mnist.nim: loads the reference pretrained ADs/thresholds and MNIST from /tmp, reproduces the reference exactly - 97.210% corrected and 96.540% bug-compatible - confirming the port. No gun/wiring integration yet; inputs and outputs to be agreed separately.
156 lines
6.8 KiB
Markdown
156 lines
6.8 KiB
Markdown
# BitBrain (ADE + SBC) — generic Nim library
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A clean-room Nim implementation of the classifier in
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> *BitBrain and Sparse Binary Coincidence (SBC) memories*,
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> Frontiers in Neuroinformatics 17:1125844, 2023.
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**This is a library, not a gun.** There is no I/O design, no battle wiring and no
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environment knobs here yet — input and output formats are to be agreed separately.
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## Clean-room note
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The implementation was written **from the published algorithm description only**.
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No code was copied from the reference C program (`full_mnist_2048.c`) shipped with
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the paper; that file is GPL-3.0-or-later, © 2022 The University of Manchester, and
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copying it would impose that licence on this repository. The published defaults
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(`scale = 64`, `centre = 127`) make the scoring rule numerically identical to the
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reference, which the MNIST acceptance test confirms to the digit.
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## Mechanism
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1. **ADE (Address Decoder Element)** — a sparse, signed, thresholded random
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projection. Each ADE has `width` synapses `(inputIndex, polarity)` and scores an
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input as
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```
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raw = Σ_j polarity_j * (input[inputIndex_j] - center)
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score = scale * raw
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```
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firing iff `score >= threshold`. Defaults `scale = 64`, `center = 127`
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reproduce the reference exactly. Multi-width ADs (the paper's best setup uses
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widths `{6, 8, 10, 12}`) detect features at different scales.
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2. **Homeostatic threshold learning** (optional, unsupervised) — accumulate each
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ADE's firing count over inputs, then nudge its threshold toward a target firing
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rate (~1% in the paper). `accumulateFiring` + `adaptThresholds` implement the
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paper's deterministic controller. The paper's additional Hebbian *longevity*
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step (retire the weakest synapse, resample a new input index) and its
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Metropolis–Hastings input-position sampling are **described in `ade.nim` but
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not implemented** — `initRandomAddressDecoder` uses the paper's uniform random
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initialisation.
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3. **SBC memory** (supervised) — two ADs on the axes; a pair of simultaneously
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firing ADEs `(i, j)` is a coincidence indexing one cell holding a class
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bitmask. `learn` **sets** the class bit and is idempotent: setting it again is a
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no-op. No clearing, no learning rate, no decay, no epochs. `infer` accesses the
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same locations but **counts** set bits per class.
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4. **BitBrain container** — several ADs (possibly different widths) plus several
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SBCs built from pairs of them. Inference aggregates class counts across SBCs;
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argmax wins (ties to the lowest class index).
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## API
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```nim
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import bitbrain/bitbrain # re-exports ade + sbc
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# --- construction ---
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var bb = buildRandomBitBrain(
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widths = @[6, 8, 10, 12], # one AD per width
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nAde = 512, # ADEs per AD
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inputWidth = 256, # input vector length
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nClasses = 8,
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seed = 1234'i64) # deterministic
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# or build ADs yourself and assemble:
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# var ad = initAddressDecoder(nAde = 2048, width = 6)
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# ad.codes = ... # signed 1-based input codes (pretrained)
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# ad.thresholds = ...
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# var bb = initBitBrain(@[ad, ...], crossPairs(4) & withinPairs(4), nClasses = 10)
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# --- online by construction (order-free, repeatable, interleaved) ---
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bb.learn(input, class) # sets class bits, idempotent
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let (label, counts) = bb.infer(input) # argmax + per-class counts
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bb.resetLearning() # wipe SBCs (ADs unchanged)
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# --- optional unsupervised homeostasis ---
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for input in trainingStream:
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ad.accumulateFiring(input)
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ad.adaptThresholds(interval = 2000, targetRate = 0.01, step = 1)
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# --- accounting ---
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bb.memoryBytes # ADs + SBC tensors
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bb.sbcMemoryBytes # SBC tensors only (the dominant term)
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```
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Generic over the input element type (`openArray[SomeInteger]`) and over the input
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width, ADE count and class count — nothing is hardcoded to 784/10. Deterministic
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given the seed. Dependencies: `std/` only.
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Reference SBC wiring: `crossPairs(4)` gives the 6 cross-AD SBCs used by the
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reference C. `withinPairs(n)` adds the paper's 4 within-AD ("half-size") SBCs;
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this implementation stores them full-size (half-size packing is a separate memory
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optimisation).
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## Measured results
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All figures measured on this machine with `-d:release`, single-threaded, on the
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reference fixtures (pretrained ADs + MNIST). Nothing is committed: fixtures live
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under `/tmp/bitbrain/BitBrain_C_code` (override with `$BITBRAIN_FIXTURES`).
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### MNIST acceptance — exact reproduction of the reference
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4 ADs × 2048 ADEs, widths `{6, 8, 10, 12}`, 6 cross-AD SBCs, 10 classes, one
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online pass over 60,000 training samples, evaluated on all 10,000 test images:
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| Reader | This library | Reference C |
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|---|---:|---:|
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| Corrected (clean-room, all row ADEs counted) | **97.210%** | 97.210% |
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| Bug-compatible (`uint8_t bit_test`: only `i % 32 < 8`) | **96.540%** | 96.540% |
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The bug-compatible mode reproduces the shipped reference's truncation bug exactly,
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which proves the ADE scoring, the coincidence indexing and the idempotent SBC rule
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are all correct. The default corrected reader is the one to use.
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### Per-sample cost (reference MNIST configuration)
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| Operation | Measured |
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|---|---:|
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| `learn` (4 ADs + 6 SBCs, dense active lists) | **0.384 ms/sample** |
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| `infer` (corrected reader) | **0.630 ms/sample** |
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| `infer` (bug-compatible reader) | 0.454 ms/sample |
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All well inside this project's live budget of ~13.16 ms/tick. The reference C
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measured ~0.2–0.56 ms/sample for the same geometry.
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### Memory (measured via `sbcMemoryBytes` / `memoryBytes`)
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The SBC tensor dominates: `nAde × nAde × nClasses` bits per SBC, rounded up to
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`uint32` slots.
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| Configuration | SBC tensors | ADs | Total |
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|---|---:|---:|---:|
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| Reference: 6 SBCs × 2048² × 10 | 31,457,280 B (30.0 MiB) | 360,448 B | 31,817,728 B (30.3 MiB) |
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| Gun-sized: 6 SBCs × 512² × 8 | 1,572,864 B (1.5 MiB) | 90,112 B | 1,662,976 B (1.59 MiB) |
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| 10 SBCs × 2048² × 10 (paper variant) | 52,428,800 B (50.0 MiB) | 360,448 B | 52,789,248 B (50.3 MiB) |
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The reference configuration is L2/L3-hostile; a battle-sized configuration should
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size `nAde` to the feature count, not copy 2048².
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## Tests
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```bash
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# Unit / sanity suite (no fixtures required, ~3 s)
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nim c -r --nimcache:/tmp/nc_j90 -d:release \
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--path:common_libs common_libs/tests/test_bitbrain.nim
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# MNIST acceptance harness (needs fixtures in /tmp; skips cleanly if absent)
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nim c -r --nimcache:/tmp/nc_j90 -d:release --path:common_libs \
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-o:/tmp/acceptance_bitbrain_mnist common_libs/tests/test_bitbrain_mnist.nim
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```
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The unit suite covers: hand-computed ADE scoring and the `>=` threshold, idempotent
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SBC learning (learn one sample 1000× → bit-identical memory), a planted rule
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learned near-perfectly with a monotone online accuracy curve, a shuffled-label
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control that degrades to chance, unseen-input behaviour, homeostatic
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threshold adaptation, and memory accounting.
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