AUTOHET: An Automated Heterogeneous ReRAM-Based Accelerator for DNN Inference

ReRAM-based accelerators have become prevalent in accelerating deep neural network inference owing to their in-situ computing capability of ReRAM crossbars. However, most existing ReRAM-based accelerators are designed with homogeneous crossbars, leading to either low resource utilization or sub-optimal energy efficiency. In this paper, we propose AutoHet, an automated heterogeneous ReRAM-based accelerator with varied-size crossbars for different DNN layers. To achieve both high crossbar utilization and energy efficiency, AutoHet uses a reinforcement learning algorithm to automatically determine the proper crossbar configuration for each DNN layer. Additionally, AutoHet introduces rectangle crossbars and a tile-shared crossbar allocation scheme to reduce crossbar wastage and energy consumption. Experiment results show that AutoHet effectively improves crossbar utilization by up to 3.1 × and reduces energy consumption by up to 94.6%, compared to approaches with homogeneous ReRAM crossbars.