A Robust 8-Bit Non-Volatile Computing-in-Memory Core for Low-Power Parallel MAC Operations
Sai Zhang, Kejie Huang, Haibin Shen
Abstract
The Artificial Intelligence (AI) in edge computing is requesting new processing units with a much higher computing-power ratio. The emerging resistive Non-Volatile Memory (NVM) with the in-memory computing capability may greatly advance the AI hardware technologies. In this paper, we propose the use of binary resistive memory to form an 8-bit fixed-point data/weight for AI computing. A robust Computing-In-Memory (CIM) core with digital input and analog output Multiplication-and-Accumulation (MAC) circuit is proposed. The corresponding integration scheme and Successive Approximation Register Analog-to-Digital Converter (SAR ADC) based data conversion scheme are also presented. The simulation results show that the proposed CIM core achieves 7.26 bit of Effective Number of Bits (ENOB) with 0.78mW (256*1) power consumption and 1.85M/s computing speed. Compared with previously reported CIM implementations and Deep Learning Accelerators (DLAs) (without CIM ability), our design achieves 2.23-7.26× better energy efficiency in 8-bit input 8-bit weight pattern, and achieves relatively high accuracy with LeNet and AlexNet.