A CFMB STT-MRAM-Based Computing-in-Memory Proposal With Cascade Computing Unit for Edge AI Devices

Abstract

The application of non-volatile memory technology is increasingly attractive for Computing-in-memory (CIM) owing to high integration density and negligible standby power consumption. This study proposes an spin-transfer-torque (STT) magnetic random access memory (MRAM) based CIM macro which incorporates following innovative features: 1) cross-feedback margin-boost (CFMB) scheme to enable robust and fast reading operations against process variation and limited Tunneling Magnetoresistance Ratio (TMR); 2) cascade computing units (CCU) and related design method for efficient and stable multi-bit multiply-and-accumulate (MAC) operation; and 3) dual computing mode scheme and resolution adjustable quantization module to optimize energy efficiency and operating speed. The post-simulations are performed under 28nm CMOS&MTJ technology. The results demonstrate the achievement in energy efficiency of 36.4 TOPS/W while performing MAC operations with up to 16-bit weights, 4-bit inputs, and 22-bit outputs.