STICKER-IM: A 65 nm Computing-in-Memory NN Processor Using Block-Wise Sparsity Optimization and Inter/Intra-Macro Data Reuse
Jinshan Yue, Yongpan Liu, Zhe Yuan, Xiaoyu Feng, Yifan He, Wenyu Sun, Zhixiao Zhang, Xin Si, Ruhui Liu, Zi Wang, Meng‐Fan Chang, Chunmeng Dou, Xueqing Li, Ming Liu, Huazhong Yang
Abstract
Computing-in-memory (CIM) is a promising architecture for energy-efficient neural network (NN) processors. Several CIM macros have demonstrated high energy efficiency, while CIM-based system-on-a-chip is not well explored. This work presents a CIM NN processor, named STICKER-IM, which is implemented with sophisticated system integration. Three key innovations are proposed. First, a CIM-friendly block-wise sparsity (BWS) architecture is designed, enabling both activation-sparsity-aware acceleration and weight-sparsity-aware power-saving. Second, an adaptive kernel-/channel-order (KCO) mapping and intra-/inter-macro scheduling strategy is proposed to improve macro utilization and data reuse. Third, an efficient BWS-optimized CIM (BWS-CIM) macro with adaptive power-OFF ADCs is implemented. The STICKER-IM chip was fabricated in 65-nm CMOS technology. Experimental results show 5.8–158-TOPS/W average system energy efficiency on the sparse NN models. The macro/system-level energy efficiency is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$4.23\times / 3.06\times $ </tex-math></inline-formula> higher compared with the state-of-the-art CIM macros and processors.