Accelerating Monte-Carlo Tree Search on CPU-FPGA Heterogeneous Platform

Abstract

Monte Carlo Tree Search (MCTS) methods have achieved great success in many Artificial Intelligence (AI) benchmarks. The in-tree operations become a critical performance bottleneck in realizing parallel MCTS on CPUs. In this work, we develop a scalable CPU-FPGA system for Tree-Parallel MCTS. We propose a novel decomposition and mapping of MCTS data structure and computation onto CPU and FPGA to reduce communication and coordination. High scalability of our system is achieved by encapsulating in-tree operations in an SRAM-based FPGA accelerator. To lower the high data access latency and inter-worker synchronization overheads, we develop several hardware optimizations. We show that by using our accelerator, we obtain up to 35 times speedup for in-tree operations, and superior scalability wrt number of parallel workers than state-of-the-art parallel MCTS implementations on CPU.

Yuan Meng

PhD Candidate in Computer Engineering

I co-optimize algorithm and hardware for deploying parallel AI workloads on heterogeneous platforms.