Sampling-Based Approximate Logic Synthesis: An Explainable Machine Learning Approach

Abstract

Recent years have seen promising studies on machine learning (ML) techniques applied to approximate logic synthesis (ALS), especially based on logic reconstruction from samples of input-output pairs. This “sampling-based ALS” supports integration with conventional logic synthesis and optimization techniques, as well as synthesis for a constrained input space (e.g., when primary input values are restricted using Boolean relations). To achieve an effective sampling-based ALS, for the first time, this paper proposes the use of adaptive decision trees (ADTs), and in particular variations guided by explainable ML. We adopt SHAP importance, which is a feature importance metric derived from a recent advance in explainable ML to guide the training of ADTs. We also include approximation techniques for ADT which are specifically designed for ALS, including don't-care bit assertion and instantiation. Comprehensive experiments show that we can achieve 39%-42% area reduction with 0.20%-0.22% error rate on average, based on 15 logic functions in the IWLS'20 benchmark suite.