FPGA Divide-and-Conquer Placement using Deep Reinforcement Learning

arXiv:2404.13061v1 Announce Type: cross
Abstract: This paper introduces the problem of learning to place logic blocks in Field-Programmable Gate Arrays (FPGAs) and a learning-based method. In contrast to previous search-based placement algorithms, we instead employ Reinforcement Learning (RL) with the goal of minimizing wirelength. In addition to our preliminary learning results, we also evaluated a novel decomposition to address the nature of large search space when placing many blocks on a chipboard. Empirical experiments evaluate the effectiveness of the learning …

abstract algorithms arrays arxiv contrast cs.ai cs.ar cs.lg fpga fpgas gate logic paper placement reinforcement reinforcement learning results search type