How small can a keyword-spotting accelerator get, and what does each bit of precision cost?

What it found

A one-hidden-layer MLP on pooled MFCC features of Speech Commands v0.02 (10 keywords plus unknown), quantised to 2–8 bit weights, mapped to a serial-MAC RTL engine that matches an integer NumPy reference bit for bit on all 40 swept configurations. Test accuracy saturates near 75% (0.752 at 128 hidden units, 4 bits); 4-bit weights are within 0.4 points of 8-bit at 64 and 128 units, 2 bits collapses at small width, and a 64-unit 4-bit design (0.722) uses about 31% of the best design’s modelled energy. Modelled classifier energy is 0.0045–0.19 µW at 4 inferences per second, far below the 1.8 mW microphone floor in ambient-wearable. Stylised: assumed 45 nm energy constants not re-checked, generic-gate area without memories, no front end, a third-party dataset mirror, and a weak feature set.

A figure from its own output

Paper, code and results

• README and method
• Experiments and results
• Code
• Tests

More in Task-specific AI accelerators

• Does a keyword-spotting accelerator fit in a Tiny Tapeout tile, and how accurate can it be?A streaming keyword-spotting engine with no weight memory, exact against an integer reference and synthesised to SKY130 on 32 geometries (6,300 to 24,500 um2); 16 hidden units at 8-bit weights is the most accurate (0.616) that fits one assumed 1x1 Tiny Tapeout tile at 70% utilisation, 64 units at 4 bits (0.738) needs two. Stylised: synthesis only.
• Under a latency deadline, how much engine area does picking the most accurate model first cost, compared with choosing model and lane count together?Model-first vs joint choice of model and MAC lane count under a latency deadline, on the 40 measured accel-kws models; accepting 2 validation points less cuts engine area 31–42% (stylised, table-based, cycles for more than one lane analytic).
• Does NSGA-II find a keyword-spotting accelerator's Pareto front faster than random search at equal budgets?NSGA-II against random search at equal budgets on a 3,840-design keyword-spotting accelerator space with exact ground truth; NSGA-II finds 54 to 89% of the exact front at 20.8% of the space against 19 to 21% for random search, though hypervolume is 0.97 to 1.00 for both. Stylised.
• Was accel-nsga's NSGA-II win a tuning accident? Does population size, mutation rate or crossover change how much of the exact front it finds?Sweeps NSGA-II's population, mutation rate and crossover on the accel-nsga space; mutation 0.02 to 0.1 finds about 0.38 and 0.71 to 0.77 of the exact front at 400 designs, against 0.28 and 0.59 at accel-nsga's setting and 0.13 and 0.12 for random search (stylised).
• Which dataflow should a small keyword-spotting accelerator use, and does the answer survive allowing an input buffer?Weight-, output-, input-stationary and reduction-tree dataflows of a P-lane MAC array on the 40 accel-kws models, from a cycle-by-cycle schedule simulator that matches closed forms exactly and the P = 1 RTL cycles; output-stationary wins on SRAM energy at batch 4 until an input buffer is allowed, then input-mapped flows win at 32 lanes (stylised, assumed energy constants, no RTL for more than one lane).
• How large must an activation buffer be before the best dataflow for a small keyword-spotting accelerator changes?How large a bounded LRU activation buffer must be before output-stationary stops being the best dataflow for the 40 accel-kws models at batch 4: 32, 64 and 128 words at 8, 16 and 32 lanes (for 10 to 30 of 40 models, input-stationary wins), and not at all at batch 1; buffer-read energy is assumed and no area is charged (stylised).

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Produced with AI assistance (Claude). Results are from the code in this repository and are simulated or stylised unless the abstract says otherwise; nothing here implies affiliation with or endorsement by any lab, researcher or company cited. MIT licensed.