A Non-Volatile Computing-In-Memory Framework With Margin Enhancement Based CSA and Offset Reduction Based ADC

Nowadays, deep neural network (DNN) has played an important role in machine learning. Non-volatile computing-in-memory (nvCIM) for DNN has become a new architecture to optimize hardware performance and energy efficiency. However, the existing nvCIM accelerators focus on system-level performance but ignore analog factors. In this paper, the sense margin and offset are considered in the proposed nvCIM framework. The margin enhancement based current-mode sense amplifier (MECSA) and the offset reduction based analog-to-digital converter (ORADC) are proposed to improve the accuracy of the ADC. Based on the above methods, the nvCIM framework is displayed and the experiment results show that the proposed framework has an improvement on area, power, and latency with the high accuracy of network models, and the energy efficiency is 2.3 - 20.4× compared to the existing RRAM based nvCIM accelerators.