A 701.7 TOPS/W Compute-in-Memory Processor With Time-Domain Computing for Spiking Neural Network

Abstract

Artificial neural networks have led to a higher computational burden, complicating inference tasks on low-power edge devices. Spiking neural network (SNN), which leverages sparse spikes for computation and data transmission, is an effective energy-efficient computing technique. However, the length of spike sequences in SNN varies significantly depending on the input coding method, among which rate coding still results in substantial data movement. A highly energy-efficient SNN accelerator with a time-domain CIM processor is proposed with three key features: 1) time-domain bitcell array for high linearity with lower energy, reducing 58.6% power consumption compared to inverter-chain architecture, 2) time-domain multi-bit accumulate for assisting multi-bit weights without analog-to-digital converter, achieving 47.2% energy reduction of domain-conversion energy, 3) analog precision reconstruction unit for supporting phase coding. The proposed TS-CIM is designed in 65 nm CMOS technology and achieves 701.7 TOPS/W energy efficiency, marking a 1.58x enhancement compared to the state-of-the-art SNN CIM.