  Optimising the quality-of-results (QoR) of circuits during logic synthesis is
a formidable challenge necessitating the exploration of exponentially sized
search spaces. While expert-designed operations aid in uncovering effective
sequences, the increase in complexity of logic circuits favours automated
procedures. Inspired by the successes of machine learning, researchers adapted
deep learning and reinforcement learning to logic synthesis applications.
However successful, those techniques suffer from high sample complexities
preventing widespread adoption. To enable efficient and scalable solutions, we
propose BOiLS, the first algorithm adapting modern Bayesian optimisation to
navigate the space of synthesis operations. BOiLS requires no human
intervention and effectively trades-off exploration versus exploitation through
novel Gaussian process kernels and trust-region constrained acquisitions. In a
set of experiments on EPFL benchmarks, we demonstrate BOiLS's superior
performance compared to state-of-the-art in terms of both sample efficiency and
QoR values.
