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

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.