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.

翻译：在逻辑合成过程中优化电路质量是一项艰巨的挑战,需要探索指数规模的搜索空间。虽然专家设计的行动有助于发现有效序列,但逻辑电路的复杂性增加有利于自动化程序。在机器学习的成功激励下,研究人员对逻辑合成应用进行了深层次的学习和强化学习。尽管这些技术取得了成功,但具有高样本复杂性,无法广泛采用。为了能够实现高效和可扩展的解决方案,我们提议BOILS,这是使现代巴伊西亚优化适应合成操作空间的第一个算法。BOILS不需要人类干预,也不需要通过新的高斯进程内核和受信任区域限制的收购,有效地进行交易勘探与开发。在一系列关于EPFL基准的实验中,我们展示了BOILS在样品效率和QOR价值方面比最新技术的优越性。