Which few designs of a new family should you synthesise to adapt a cell-count surrogate, and should they count for more?
research/eda-adaptpick
Which few designs of a new family to synthesise when adapting a cell-count surrogate: a farthest-point spread in feature space roughly halves the linear model's error against random draws at 5 designs (5.3% against 8.5%), the smallest designs are worst, and upweighting new points helps linear and ridge but not boosted trees (stylised, synthesis-only target).
What it found
A follow-up to eda-adapt on the same 133 designs in four generator families. Choosing the k adaptation designs by farthest-point spread in feature space, instead of at random, roughly halves the linear model’s held-out median error at k = 5 (5.3% against 8.5%, new-family weight 5); picking the smallest designs is worst (15.2%). Upweighting the new family’s points helps the linear and ridge models from k = 5 but hurt boosted trees. Stylised: four small clean families, a synthesis-only cell-count target, deterministic choosers scored on different remaining sets, no confidence intervals.
A figure from its own output
Paper, code and results
More in Algorithmic chip-design loops
- Does the farthest-point spread still beat random draws when every chooser is scored on the same held-out designs?
eda-adaptpick's adaptation-design choosers re-scored on one fixed test set per family (4 families x 5 splits): the farthest-point spread still beats random draws for the linear model at every k (4.8% against 6.0% at 5 designs, w = 5) but wins in only 65% of cells, the smallest-first rule stays worst, and the earlier 'halves the error' margin shrinks to 20-45% at 5 to 8 designs. - Does a cell-count surrogate used as a screen find a chip design’s Pareto front with fewer syntheses than random search or TPE?A cell-count surrogate used as a screen in the design-search loop: 4 spread syntheses plus a log-linear fit reach 0.990 of the true front's hypervolume at 12 syntheses against 0.860 for random and 0.878 for TPE, of which about +0.05 comes from free cycles/error objectives (stylised, one 80-design space).
- How much error in a cheap objective can design-search proposers tolerate before they stop beating random search?With Gaussian error on the objectives a proposer sees, TPE's budget-30 lead over random falls from +0.037 to +0.006 (gone at error 0.8 of the objective range), NSGA-II and the genetic method lose theirs by 0.1, annealing is never better (stylised, synthetic noise, one 80-design space).
- Does the ranking of design-search proposers survive a change of hypervolume reference point or quality indicator?TPE stays best under five hypervolume reference points, recall, IGD+ and epsilon, but its lead over random at budget 30 ranges from +0.045 to +0.002 with the reference point (stylised, one 80-design space).
- How much of a surrogate screen’s lead over random search comes from the free objectives?A random search that uses the free cycles/error objectives reaches 0.901 of the true front's hypervolume at 12 syntheses (random 0.860, screen 0.990), so +0.04 of the screen's lead is the free objectives and +0.09 the cell-count surrogate (stylised, one 80-design space).
- Can a cost model trained on other chip-design families screen a new family’s Pareto front with a handful of syntheses?A Pareto screen whose cell-count model was trained only on other design families reaches 0.94 to 0.98 of the true front's hypervolume with 3 Yosys syntheses (random about 0.65), though the model's absolute error is large; ranking is what matters (stylised, MAC and FIR spaces of 27 designs).
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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.