Analog Compute in Memory and Breaking Digital Number Representations

Abstract

Analog in Memory Computing (AiMC) is a promising solution to achieve energy efficient deployment of Deep Neural Networks (DNNs). However, to this day, the accuracies of DNNs deployed using AIMC do not compare favorably with digital alternatives. In this work, we illustrate that these lacking performance can be partly attributed to mismatched arithmetic assumption. We show that correcting those assumptions results in more robust and accurate DNNs on analog platforms. By further leveraging insights from AiMC related methods, we question modern neural network quantization techniques, revealing, in the process, alternative multiplication-less modes of executions for DNNs.