Does Tiny Tapeout's real tile-size table change which keyword-spotting model fits a tile?

What it found

accel-ttfit assumed a 161 × 112 µm tile from a search snippet. This project fetches Tiny Tapeout's own SKY130 tile table (1x1 = 17,955 µm², 0.43% smaller) and re-fits the synthesised keyword-spotting engine (6,300–24,500 µm²) with the retrained accuracies of accel-ttretrain over all 12 tile sizes at 100, 70 and 50% utilisation. One 1x1 tile: the best model is unchanged at 100% (H = 32, 8-bit, 0.693) and 50% (H = 16, 3-bit, 0.563) and flips at 70% (H = 16, 4-bit, 0.596, because the 8-bit model misses by 29 µm²). A 1x2 tile holds H = 64 with 8-bit weights (0.737), the largest model swept. Utilisation is assumed, area is synthesis-only, accuracy is on a substitute feature set; the tile table itself is sourced.

Paper, code and results

• README and method
• Experiments and results
• Tests

More in Task-specific AI accelerators

• How small can a keyword-spotting accelerator get, and what does each bit of precision cost?Keyword-spotting accelerator: quantised MLP on Speech Commands, serial-MAC RTL bit-exact against an integer reference, accuracy vs cycles, cells and modelled energy over width and weight bits (stylised).
• 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).

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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.