  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. This approach on achieves 2x-10x
run-time improvement and better quality-of-result (QoR) than state-of-the-art
machine learning approaches.
