SSRScientific and Social Research2661-43322981-9946Bio-Byword Scientific Publishing Pty. Ltd.10.26689/ssr.v8i9.15780ArticleCross-Design Generalization of Lightweight Surrogate Models for Predicting Logic Synthesis Qualityhttps://artdesignp.com/journal/SSR/8/9/10.26689/ssr.v8i9.15780HuangJunyan2026892026-10-21The quality of results (QoR) of logic synthesis depends on the type and order of the optimization operators, and exhaustive evaluation is expensive, so machine-learning surrogates are widely used to predict recipe QoR. In the reference train and test lists released with the public dataset OpenABC-D, every design in the test list also appears in the training list, so figures reported on those lists need not reflect transfer to a new design. Using that dataset, this study takes 27 open-source circuits and 1,500 twenty-step ABC recipes, forming 40,500 labeled runs, trains four lightweight surrogates on 30 features, and scores them within each design under a random split and under leave-one-design-out (LODO). The median within-design mean absolute percentage error for area rises from 2.66% to 23.30% between the two protocols and falls back to 3.54% when the target is expressed relative to one reference-recipe calibration, although the coefficient of determination stays below zero. Under LODO, the median within-design rank correlation for area is 0.547, positive for 24 of the 27 held-out designs, so ranking information is partly transferable; for delay it is 0.007 (P=0.097) and no significant transfer is detected. Gradient boosting ranks best and trains in 0.161 seconds per fold. The evaluation protocol markedly changes apparent accuracy, and cross-design use of these features is supported for ranking area, not delay.Logic synthesis, Quality of results prediction, Surrogate model, Cross-design generalization, And-inverter graph

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