Bulls-Eye: Active Few-Shot Learning Guided Logic Synthesis

Abstract

Generating sub-optimal synthesis transformation sequences (“synthesis recipe”) is an important problem in logic synthesis. Manually crafted synthesis recipes have poor quality. State-of-the art machine learning (ML) works to generate synthesis recipes do not scale to large netlists as the models need to be trained from scratch, for which training data is collected using time consuming synthesis runs. We propose a new approach, Bulls-Eye, that fine-tunes a pre-trained model on past synthesis data to accurately predict the quality of a synthesis recipe for an unseen netlist. Our approach achieves 2x-30x run-time improvement and generates synthesis recipes achieving close to 95% quality-of-result (QoR) compared to conventional techniques using actual synthesis runs. We show our QoR beat state-of-the-art approaches on various benchmarks.