SSM-CIM: An Efficient CIM Macro Featuring Single-Step Multi-bit MAC Computation for CNN Edge Inference

Heng Zhang, Sunan He, Xin Lu, Xinjie Guo, Shaodi Wang, Yuan Du, Li Du

Abstract: Compute-in-memory (CIM) is a promising approach to solving the memory-wall problem existing in traditional computing architectures. In this paper, we introduce SSM-CIM, a charge-domain, static random-access memory (SRAM)-based CIM macro designed for area-energy-efficient convolutional neural network (CNN) inference. SSM-CIM utilizes an original sign-magnitude data encoding method for both inputs and weights. By codesigning four adjacent SRAM computing cells and employing a 3-bit digital-to-analog converter (DAC), SSM-CIM performs accurate 4-bit multiply-and-accumulate (MAC) computation in a single step, eliminating the peripheral digital shift-and-add circuits. To digitize the MAC computing results, a dedicated multi-reference assisted SAR ADC is designed by reusing the reference voltages from the DAC, which offers significant power and area savings. In addition, analog computing errors and quantization errors are analyzed to ensure the multi-bit computing accuracy of SSM-CIM. SSM-CIM is implemented and evaluated using 28-nm global foundry process. The post-layout simulation results validate the excellent computing linearity and accuracy of SSM-CIM. Benefitting from the compact layout design and fully parallel computing flow, the $144\times 256$ macro achieves a peak throughput of 2.3 TOPS, an area efficiency of 10.2 TOPS/mm2, and an energy efficiency of 205.4 TOPS/W with 4-bit weights and 4-bit inputs.