A Multibit ReRAM Computing-in-Memory Processor with Adaptive Decision Level Nonlinear ADC for Ultra-low-energy Keyword Spotting in Mobile Devices

Abstract

This paper presents an ultra-low-energy keyword spotting (KWS) processor based on the charge-mode multi-level resistive random-access memory (ReRAM) bitcell and adaptive analog-to-digital converter (ADC) quantization. Previous ReRAM computing-in-memory (CIM) architectures have suffered several challenges, including excessive computing energy due to direct current branches, computation non-linearity, and throughput degradation resulting from analog-to-digital conversion. The proposed processor addresses these challenges by introducing a charge-mode multi-bit ReRAM bitcell (CRB), which achieves a 61.39% reduction in multiply-and-accumulate (MAC) energy. The CRB also enhances MAC linearity by 1.65×. Additionally, the 5-bit additive powers-of-two ADC achieves a 65.42% reduction in analog-to-digital conversion energy, an 11.70 percentage points decrease in ADC accuracy loss, and a 1.33× increase in conversion throughput. Furthermore, the pipelined layer fusion clusters reduce intermediate data movement energy to 97.2 nJ and enhance KWS system throughput by 1.35×. The proposed processor is designed utilizing 45 nm CMOS technology and compatible ReRAM devices. The processor occupies an area of 0.94 mm2 with a 68.5 KB ReRAM cell. The processor also achieves 0.74~\mu $ J/decision energy consumption, 22.59 TOPS/W energy efficiency, and 92.7% accuracy on the Google Speech Commands Dataset.