Do the accel-* energy constants match Horowitz (2014), and does fixing the array-size term change accel-kws's conclusions?

research/accel-horowitzcheck

All 34 energy constants in the accel-* projects that have a counterpart in Horowitz's Fig. 1.1.9 are within 4% of it; re-costing accel-kws with the figure's array-size law (energy about the square root of size) moves energy to 0.23 to 0.81 of as built but keeps 17 of 18 front designs (Spearman 0.991)

7 tests

What it found

free-citeaudit found Horowitz (2014) among the most-repeated unchecked citations. This project reads Fig. 1.1.9 of the paper (45 nm, 0.9 V) and compares it with every top-level E_* constant in research/accel-*/src: all 34 with a counterpart are within 4% (the 24-bit add is 0.963 of a log-log interpolation, the 0.16 pJ/bit array read is 1.024 times 10 pJ per 64 bits); one constant, a gating energy, has none. The figure's multiplier energy has a width slope of 1.98 (so the repository's bits-squared rule is right) and its add slope is 0.87. What the repository did not model is array size: the figure's read energy grows with a slope near 0.5 against size, while accel-kws charges a flat 0.16 pJ/bit. Re-costing its 40 designs (reads are 90 to 93% of modelled energy) with the size law, extrapolated below 8 KB where the paper has no point, gives 0.23 to 0.81 of the as-built energy, a rank correlation of 0.991 and 17 of 18 front members kept; clamping at the 8 KB point instead gives 0.98 to 1.18 and the same front. Limitations: a processor-cache table, not a weight memory; the sub-8 KB law is an extrapolation; only accel-kws was re-costed; the figure was read from a third-party copy.

A figure from its own output

Figure produced by research/accel-horowitzcheck: energy — accel-horowitzcheck/experiments/energy.png, written by the project's own experiment code.

Paper, code and results

More in Task-specific AI accelerators

Does weight-sharing lookup-table inference beat an int8 MAC engine once small arrays cost less to read?Pricing every array by its own size flips accel-lut's negative result once read energy grows faster than size^0.26 (dense) or size^0.28 (pruned); Horowitz's slope is 0.5 above 8 KB but there is no point below it, so the verdict is conditional
Is the OpenRAM access energy in SKY130 liberty files an energy at all, and does accel-pdkprice's weight-stationary verdict survive?OpenRAM's clk0 figure is an average power in mW over one cycle of an unstated period, not pJ: the unpacked verdict (register-file WS wins) holds for any period above 3 ns, while the packed verdict flips between 1 and 31 ns, so accel-pdkprice's packed conclusion is conditional
Once the MFCC front end is counted, does the energy-optimal keyword model change?Joining accel-cascade's committed results with accel-frontend's 46.8 nJ per frame: cascades' classifier-only saving of up to 28% becomes at most 0.42% of whole-system energy, while the most accurate model (about 0.75 test accuracy) costs 23 to 26 times the cheapest classifier but only 5% more system energy (modelled, not measured)
How big a keyword-spotting model fits a real Tiny Tapeout tile once the hidden layer goes past 64 units?Synthesising the streaming keyword-spotting engine at 96, 128, 192 and 256 hidden units (SKY130 HD, Yosys): area grows about 250 µm² per unit, so H = 128 (0.752 accuracy, 4-bit) needs a 2x2 tile (75,603 µm²) against a 1x2 (36,347 µm²) for H = 64 (0.737): +0.015 accuracy for twice the tile; H = 256 needs 4x2 at 50% utilisation, and its 8-bit layer-2 accumulator overflows 20 bits on a random model.
Do trained keyword-spotting models fit a Tiny Tapeout tile's 16-bit bias and 20-bit accumulator?All 96 retrained keyword-spotting models fit the Tiny Tapeout engine's 16-bit bias and 20-bit accumulator limits (worst use 43% and 30%), accuracy through the tile contract equals the reference, and 32 trained models run bit-exact in RTL.
Does Tiny Tapeout's real tile-size table change which keyword-spotting model fits a tile?Tiny Tapeout's real SKY130 tile sizes (1x1 is 161 × 111.52 µm = 17,955 µm², 0.43% below the value assumed earlier) applied to the synthesised keyword-spotting engine and the retrained models: the best 1x1 model is unchanged at 100% and 50% utilisation and flips at 70% (H = 16, 4-bit, 0.596 instead of 8-bit, 0.616, a 29 µm² miss); H = 64 with 8-bit weights (0.737) fits a 1x2 tile.

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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.