Redundancy and Analog Slicing for Precise In-Memory Machine Learning—Part II: Applications and Benchmark

Abstract

In-memory computing (IMC) is attracting interest for accelerating data-intensive computing tasks, such as artificial intelligence (AI), machine learning (ML), and scientific calculus. IMC is typically conducted in the analog domain in crosspoint arrays of resistive random access memory (RRAM) devices or memristors. However, the precision of analog operations can be hindered by various sources of noise, such as the nonlinearity of the circuit components and the programming variations due to stuck devices and stochastic switching. Here we demonstrate high-precision IMC by a custom program-verify algorithm that uses redundancy to limit the impact of stuck devices and analog slicing to encode the analog programming error in a separate memory cell. The PageRank problem, consisting of the calculation of the principal eigenvector, is shown as a reference problem, adopting a fully integrated RRAM circuit. We extend these results to also include a convolutional neural network (CNN). We demonstrate a computing accuracy of 6.7 equivalent number of bits (ENOBs). Finally, we compare our results to the solution of the same problem by a static random access memory (SRAM)-based IMC, showcasing an advantage for the RRAM implementation in terms of energy efficiency and scaling.