How many synthesised designs from a new family does a cell-count surrogate need before it is usable, and is it better to pool them with old families or train on them alone?
research/eda-adapt
Few-shot adaptation of the eda-surrogate cell-count model to an unseen design family: one synthesised design from the new family cuts the linear model's median error from 49% to 18%, training on the new family alone beats pooling from about three designs, boosted trees need about 12 (stylised, synthesis-only target, no place and route).
What it found
A follow-up to eda-surrogate on its 133 designs in four generator families: when a word-level cell-count surrogate meets a family it has not seen, how many Yosys syntheses of that family restore it? Over 30 random draws per cell, one synthesised design pooled with the other families cuts the linear model’s median error from 48.6% to 18.4% (median over the four families), and training on the new family alone overtakes pooling from about three designs (4.8% against 10.4% at five). Boosted trees need far more (about 12 designs to get under 20%) and pooled boosting is worse than zero-shot on one family for up to five designs. Ranking is good throughout. Stylised: small clean families, random rather than chosen adaptation designs, a synthesis-only target, no place and route.
A figure from its own output
Paper, code and results
More in Algorithmic chip-design loops
- Which few designs of a new family should you synthesise to adapt a cell-count surrogate, and should they count for more?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).
- 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).
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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.