A 4-Kb 1-to-8-bit Configurable 6T SRAM-Based Computation-in-Memory Unit-Macro for CNN-Based AI Edge Processors Y en-Cheng Chiu, Zhixiao Zhang, Jia-Jing Chen, Xin Si , Student Member , IEEE, Ruhui Liu, Y

Abstract

Previous SRAM-based computing-in-memory (SRAM-CIM) macros suffer small read margins for high- precision operations, large cell array area overhead, and limited compatibility with many input and weight configurations. This work presents a 1-to-8-bit configurable SRAM CIM unit-macro using: 1) a hybrid structure combining 6T-SRAM based in-memory binary product-sum (PS) operations with digital near-memory-computing multibit PS accumulation to increase read accuracy and reduce area overhead; 2) column-based place- value-grouped weight mapping and a serial-bit input (SBIN) mapping scheme to facilitate reconfiguration and increase array efficiency under various input and weight configurations; 3) a self-reference multilevel reader (SRMLR) to reduce read-out energy and achieve a sensing margin 2 × that of the mid- point reference scheme; and 4) an input-aware bitline voltage compensation scheme to ensure successful read operations across various input-weight patterns. A 4-Kb configurable 6T-SRAM CIM unit-macro was fabricated using a 55-nm CMOS process with foundry 6T-SRAM cells. The resulting macro achieved access times of 3.5 ns per cycle (pipeline) and energy efficiency of 0.6–40.2 TOPS/W under binary to 8-b input/8-b weight precision.