PattPIM: A Practical ReRAM-Based DNN Accelerator by Reusing Weight Pattern Repetitions
Yuhao Zhang, Zhiping Jia, Yungang Pan, Hongchao Du, Zhaoyan Shen, Mengying Zhao, Zili Shao
Abstract
Weight sparsity has been explored to achieve energy efficiency for Resistive Random-access Memory (ReRAM) based DNN accelerators. However, most existing ReRAM-based DNN accelerators are based on an overidealized crossbar architecture and mainly focus on compressing zero weights. In this paper, we propose a novel ReRAM-based accelerator — PattPIM, to achieve space compression and computation reuse by studying DNN weight patterns based on practical ReRAM crossbars. We first thoroughly analyze the weight distribution characteristics of several typical DNN models and observe many non-zero weight pattern repetitions (WPRs). Thus, in PattPIM, we propose a WPR-aware DNN engine and a WPR-to-OU mapping scheme to save both space and computation resources. Furthermore, we adopt an approximate weight pattern transform algorithm to improve the DNN WPRs ratio to enhance the reuse efficiency with negligible inference accuracy loss. Our evaluation with 6 DNN models shows that the proposed PattPIM delivers significant performance improvement, ReRAM resources efficiency and energy saving.