A 0.74 μJ/decision and 22.59 TOPS/W Keyword Spotting CIM Processor with Short-Current-Free Multi-level ReRAM and Adaptive-Decision-Level Nonlinear ADC

An ultra-low energy keyword spotting (KWS) processor based on multi-level ReRAM computing-in-memory (CIM) architecture is proposed in this paper. Previous ReRAM-CIMs suffered from several challenges, such as considerable computation energy due to voltage-based bitcell computation, significant energy consumption and throughput overhead from analog-to-digital (A-to-D) conversion. The proposed processor addresses these challenges by introducing charge-based multi-bit computation ReRAM bitcell, which achieves a 61.39% reduction in MAC computation energy and enhances MAC linearity by 1.65×. Additionally, the proposed 5-bit additive powers-of-two nonlinear ADC leads to a 65.42% reduction in A-to-D conversion energy, a 93.57% decrease in ADC quantization error, and a 1.33× increase in ADC throughput. Furthermore, the pipelined layer fusion clusters reduce the energy consumption for data movement to below 1% and improve the system throughput by 1.35× during keyword spotting inference. The proposed ReRAM-CIM processor is designed in 45 nm technology and occupies a 0.94 mm<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> area with a 68.5 KB ReRAM cell. The proposed processor also achieves 0.74 μJ/decision energy consumption with 92.7% accuracy on the Google Speech Commands dataset.