On Accelerating Domain-Specific MC-TS with Knowledge Retention and Efficient Parallelization for Logic Optimization

Recently, demand of higher quality of result (QoR) for logic optimization have spurred numerous studies on generating logic transformation sequence for specific objective. Nevertheless, previous works often suffer from heavy reliance on computing resources or are stuck at local optima. In this work, we propose a logic transformation sequence generator based on domain-specific Monte Carlo tree search (MC- TS). Our framework propose to utilizes information obtained from synthesis tools as context to guild the tree search. Additionally, we implement the algorithm with knowledge retention and adaptive parallelization for further acceleration. Experiments on open-source benchmarks of various scale show that our framework outperforms the state-of-the-art work, Alphasyn, with an average 20.11 % efficiency improvement while maintaining comparable effectiveness.