A Multichiplet Computing-in-Memory Architecture Exploration Framework Based on Various CIM Devices

Zhuoyu Dai, Feibin Xiang, Xiangqu Fu, Yifan He, Wenyu Sun, Yongpan Liu, Guanhua Yang, Feng Zhang, Jinshan Yue, Ling Li

Abstract: Computing-in-memory (CIM) architectures based on various devices, such as resistive random access memory, SRAM, DRAM, etc., have demonstrated promising energy efficiency. Single-device-based CIM chips show different advantages on performance, power, or area metrics under different workload/operators sizes and application requirements. Some nonidealities, such as the write endurance of some nonvolatile devices, also influence the design choices. Motivated by the emerging 2.5-D/3-D chiplet integration, this work aims to combine the advantages of CIM/storage chips based on different devices, and proposes a design exploration framework to combine the advantages of CIM chips based on these devices in a 3-D-stack architecture. This work proposes: 1) an evaluation method for the power, performance, and area metrics of the multichiplet CIM architecture; 2) an abstraction for the single-device-based CIM chiplets and artificial intelligence algorithm operators; and 3) a mapping and optimization strategy to explore the 2.5-D/3-D CIM chiplet set. The effectiveness of the mapping strategy is verified with a small-scale brute-force search. The proposed design exploration framework can help to find a better-multichiplet CIM architecture. Under a simple design case, the proposed 3-D CIM architecture shows $4.68\times $ – $53.32\times $ energy efficiency compared with the single CIM chip baselines. The abstracted chiplet library is open-source available in the open-source https://github.com/dai0dai/3D_CIM_Chiplet_Architecture_Exploration.