AOME: Autonomous Optimal Mapping Exploration Using Reinforcement Learning for NoC-based Accelerators Running Neural Networks

Hardware mapping plays a critical role in the performance of NoC-based accelerators running large-scale neural networks (NN). Confronted with enormous mapping exploration space, traditional algorithms may find sub-optimal solutions. We conduct preliminary experiments to investigate the impact of different hardware mappings on communication latencies. Then, this paper proposes an Autonomous Optimal Mapping Exploration (AOME) architecture based on two reinforcement learning algorithms. Combining soft and hard constraints, AOME transforms the mapping process into a sequential decision problem and targets to explore the optimal mapping of the NoC system. We evaluate the performance of AOME on ten NNs. The results show that compared with the direct X mapping, the direct Y mapping, GA-base mapping, and NN-aware mapping, AOME reduces the average communication latency of ten NNs by 27.30%, 33.33%, 4.27% and 12.46% using A2C, by 27.19%, 33.21%, 4.11% and 12.31% using PPO, and improves the average communication throughput by 43.24%, 63.60%, 5.17% and 14.83% using A2C, by 43.18%, 63.68%, 5.23% and 14.87% using PPO.