CB-EVO: Contextual Bandit Tuning with Evolutionary Search for Logic Synthesis

In logic synthesis, pre-optimization involves applying a sequence of transformations, referred to as a synthesis flow, to reduce the complexity of a circuit’s Boolean logic graph, such as the And-Inverter Graph (AIG). The primary challenge lies in selecting and ordering these transformations, a task complicated by the exponentially large solution space. In this work, we propose CB-EVO, a novel online learning framework that combines contextual bandit tuning with an evolutionary algorithm to efficiently explore the solution space and generate high-quality synthesis flows. The Syn-LinUCB algorithm functions as the pivotal agent in our bandit tuning process, incorporating circuit-specific characteristics and leveraging long-term payoffs to guide decision-making effectively, thereby circumventing the pitfalls of local optima. To complement the bandit algorithm, we design an evolutionary algorithm that accelerates the decision-making process and enables the efficient expansion of the sequence. Moreover, we explore practical heuristics, such as the “return-back” mechanism and the utilization of a choice network, to further enhance optimization outcomes. Experimental results demonstrate that our framework identifies the optimal synthesis flow with reduced computational time, significantly decreasing the number of AIG nodes and LUT count compared to state-of-the-art (SOTA) methods.