What does a product-table inference engine cost in RTL, against a serial MAC engine?

What it found

Both engines for a 64×N dense layer with 4-bit activations and int8 shared weights, written in Verilog, checked bit for bit against an integer NumPy reference, and synthesised with Yosys to generic gates (assumed gate-equivalent weights). Negative and stylised: the table engine keeps one accumulator per output, so its datapath is 2.4× (N=8) to 11.6× (N=128) the MAC engine's and it takes 1.1× to 3.0× the cycles, even though it stores half the weight bits. Random weights, area only, no energy measured.

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 tiny always-on keyword model that wakes a bigger one beat the best single model on energy?Classifier-only energy of two-stage keyword-spotting cascades vs 40 single MLPs retrained on the original Speech Commands v0.02 (5 seeds, 200 models, about 41,000 cascades per seed, thresholds chosen on validation): cascades cut modelled energy by 0 to 28% at the same validation target, losing 0.3 to 1.4 test points, and the saving is under 1% of a system whose MFCC front end costs about 50 times the classifier
• Does the MFCC front end cost more than the keyword-spotting network it feeds?An instrumented Q15 MFCC front end (512-point FFT, 40 mel bands, DCT) costs 7,532 multiplies per 20 ms frame against the 5,184-MAC accel-kws classifier per inference; under assumed 45 nm constants the front end is 8 to 150 times the classifier's power, and 16-bit FFT scaling decides its accuracy (synthetic frames, no Speech Commands)
• Does block floating point fix the 16-bit FFT scaling problem in an always-on MFCC front end?A shared data-chosen exponent for the 16-bit FFT in accel-frontend's MFCC path cuts cepstral error to 0.016 to 0.081 of the spread at -60 to -30 dBFS (one shift per stage: 0.35 to 0.72) for about +0.5% modelled energy, but no better than 18 to 20-bit fixed schedules on loud input (synthetic frames, no Speech Commands)
• Do the accel-* energy constants match Horowitz (2014), and does fixing the array-size term change accel-kws's conclusions?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)
• 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

All 40 Task-specific AI accelerators projects →

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Proper scoring & elicitation28Effort & peer prediction9Verification games & slashing19Detection & tolerances14Markets as routers13Prediction-market design9Paying contributors8Decentralised training systems23Digital twins & sim-to-real164LLM chip design loops40Robot manufacturing loops53Ambient AI assistant46Self-replicating manufacturing34Simulation-first design64Engineered clothing45Task-specific AI accelerators40Algorithmic chip-design loops42Open-source purpose-built robots44The software-engineering loop43Adversarial human imitation41Open-source residential construction41Autonomous multi-angle capture34Sensing, imaging and 3D31Energy supply and dense energy storage39Robot societies among people31Agentic co-design of low-cost robots27Laundry-hanging robots25Machine learning on qualitative data23Robots on the building site27Civilizations & group psychology1History & human sciences1

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.