Can a quantised MLP surrogate of a finite-element beam beat the beam's own model-form error, and when would an accelerator for it pay?
research/accel-surrogate
Can a quantised MLP surrogate of an FE cantilever beat the Euler-Bernoulli vs Timoshenko gap (0.09 % median), and would an accelerator pay? No: float error is 1.7 % (18x the gap), 6-bit QAT stays within 0.3 points of float, and the surrogate only beats the solver above ~1,600 simulator flops. Stylised.
What it found
A 8-32-32-1 MLP imitates an Euler-Bernoulli finite-element cantilever with eight section heights. Shear deformation (Timoshenko vs Euler-Bernoulli) moves the tip deflection by a median 0.09 %; the float surrogate's median error is 1.66 %, about 18× larger, and quantisation adds little at 6 bits with quantisation-aware training (1.95 %) but 21 points at 3 bits post-training. Modelled inference is 3.7 nJ at 8 bits against about 407 nJ for the CPU solve, but the break-even is ~1,600 simulator flops, which a closed-form beam does not reach. Stylised: one tiny network, assumed energy constants, no RTL.
Paper, code and results
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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.