Abstract
ReRAM-based accelerators have become prevalent in accelerating deep neural network inference owing to their in-situ computing ca- pability 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 ap- proaches with homogeneous ReRAM crossbars.