A 40-nm SONOS Digital CIM Using Simplified LUT Multiplier and Continuous Sample-Hold Sense Amplifier for AI Edge Inference

Digital computing in memory (CIM) exhibits high precision as well as high energy efficiency (EE) yet still lacks discussion in nonvolatile memory (NVM). In this article, we propose a 40-nm silicon-oxide-nitride-oxide-silicon (SONOS)-based digital NVM CIM macro (DNV-CIM) featuring: 1) a simplified lookup table multiplier (SLUTM) combined with a lookup table (LUT) mapping scheme to improve area and EE and 2) a continuous sample-hold sense amplifier (CSH-SA) with an optimized voltage clamper and comparator for continuous read to reduce overall energy and time consumption for deep neural network (DNN) inference tasks. Performance evaluations indicate that the proposed DNV-CIM can achieve 93.04% accuracy and an EE up to 39.9 TOPS/W when running a 4-bit quantized ResNet18 trained on the CIFAR-10 dataset. This work presents a highly efficient digital CIM solution that can be readily implemented with commodity NVM.