Does "the mutant synthesises to the golden netlist" predict formal equivalence on a second design family?

What it found

Follow-up to eda-conefeat on a design the idea was not fit on: a 2×4 systolic array from chip-systolic is mutated, 32 of 143 compiling mutants survive random simulation, and Yosys tries to prove them equivalent (12 proved, 20 timeouts at 60 s/120 s, none killed). An identical post-synthesis cell histogram is always right (3 of 3 across this design and the MAC engine) but finds only 3 of the 28 proved equivalents, so it saves about 3% of formal CPU; equivalent mutants usually change the netlist by −75 to +131 cells, and a zero change is as common among timeouts as among proofs. Two designs, short timeouts on the new one. Stylised.

Paper, code and results

• README and method
• Experiments and results
• Code
• Tests

More in Algorithmic chip-design loops

• Do synthesized-netlist features predict which simulation survivors Yosys can prove equivalent?Tests whether Yosys netlist features (cell-count change, miter size) predict which eda-verify survivors are provably equivalent: none beats the 59.3% majority class under leave-one-line-out, against 92.6% for source line (negative, stylised, one design).
• Do fan-in cone features of a mutant predict which simulation survivors Yosys can prove equivalent?Fan-in cone features (changed sinks, cone gates and depth, registers reached) from hashed gate-level netlists vs which eda-verify survivors are provably equivalent: none beats the 59.3% majority class under leave-one-line-out (best 13 of 27; negative, stylised, one design).
• Is the farthest-point spread's lead over random draws, when adapting a chip-design cost model to a new family, bigger than split noise?eda-adaptfixed re-run with 40 splits per family and bootstrap intervals: for the linear model the spread beats random draws at every k (about 1 point of error at 3 to 8 designs, 2.5 at 2), but for ridge it loses at 3 designs and one family (sorter) reverses the sign, so the pooled interval is narrower than the real uncertainty.
• Does the farthest-point spread's lead over random draws, when adapting a chip-design cost model to a new family, survive resampling the designs themselves?eda-adaptci re-run with a design-level bootstrap: the intervals widen by up to 2.5x, the spread's lead holds at 5 and 8 designs for both models (about 0.5 to 1.1 points) and at 2 for the linear model, but the linear k = 3 lead and the ridge k = 2 lead include zero, and the ridge k = 3 loss reported earlier is not established.
• How many placed designs from a new family does an area surrogate need, and does the answer change when the target is placed area instead of a synthesis cell count?The eda-adapt few-shot curves refitted to OpenROAD-placed area on 133 SKY130 designs: an unseen family's placed area is off by 39% (ridge), one placed design cuts it to 23%, about three reach 20%, as for cell counts.
• Do input-biased random vectors (extreme, sparse, one-hot) catch RTL bugs that uniform random vectors never did?Four biased generators (20,000 vectors per target) catch none of the 26 eda-deepdraw survivors, so none contradicts a formal equivalence proof, and the del@122 mutant is confirmed as truncated output (90 rows for 100 vectors).

All 42 Algorithmic chip-design loops projects →

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.