Does a small control network need an accelerator to meet the control-rate deadline?

What it found

Small MLP policies, cloned from a computed-torque controller for a two-link arm with DAgger and quantised to 8 and 4 bits, with inference latency modelled for microcontroller loops and P-lane accelerators (assumed constants) and the closed loop simulated with that latency. Preliminary and mostly negative: the int8 policies track at least 300× worse than the teacher, which itself barely notices a sample of delay, so policy accuracy rather than hardware speed limits control; the deadline model says hardware only matters above about 10 kHz or for larger networks. Stylised; hardware figures are not 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 the keyword-spotting engine fit a small open-toolchain FPGA, and how fast does it clock?The accel-kws serial-MAC engine through Yosys and nextpnr-ice40 on UP5K and HX8K for 15 geometries (random weights, synthesis only): all fit in at most 22% of the UP5K's logic, weights land in block RAM, Fmax 15–22 MHz on UP5K. Stylised; no board, no power.
• When does a task's accuracy tolerance let an accelerator drop its multipliers?Pruning, int4/int8, ternary and binary MLPs on scikit-learn digits (substitute task) with a modelled-energy engine: when does the accuracy slack let the hardware drop multipliers? Stylised; at 1–2 points tolerance, pruned int8 still beats multiplier-free.
• Does the pruning and ternary-weight result from digits hold on real keyword-spotting features?accel-sparse's pruning, int4/int8, ternary and binary sweep re-run on real Speech Commands MFCC features: no multiplier-free design is within 5 points of float32 (ternary 19 down), where digits said 1; int8 pruned 50% is the cheapest within 2 points. Stylised, untuned negative.
• Does weight-sharing lookup-table inference beat a multiply-accumulate engine for a small accelerator?Weight-sharing product-table inference against int8 MAC engines on the digits MLP: negative at this size, break-even only for wide layers with cheap table reads. Stylised, modelled energy.
• Does a wider hidden layer make weight-sharing product-table inference beat an int8 MAC engine?Product-table inference on trained wide digits MLPs (32, 128, 512 hidden): at best 14% under int8 energy, only with an assumed cheap table read. Stylised, modelled energy.
• Does fine-tuning the shared weight values rescue low-K lookup-table inference?Fine-tuning the shared values of accel-lut's product-table engine: rescues K=2 in the 75%-pruned digits MLP (14% under int8 energy only at an assumed cheap table read), not the dense network. Stylised, modelled energy.

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Other research topics

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.