Bring memristive in-memory computing into general-purpose machine learning: A perspective

Abstract

In-memory computing (IMC) using emerging nonvolatile devices has received considerable attention due to its great potential for accelerating artificial neural networks and machine learning tasks. As the basic concept and operation modes of IMC are now well established, there is growing interest in employing its wide and general application. In this perspective, the path that leads memristive IMC to general-purpose machine learning is discussed in detail. First, we reviewed the development timeline of machine learning algorithms that employ memristive devices, such as resistive random-access memory and phase-change memory. Then we summarized two typical aspects of realizing IMC-based general-purpose machine learning. One involves a heterogeneous computing system for algorithmic completeness. The other is to obtain the configurable precision techniques for the compromise of the precision-efficiency dilemma. Finally, the major directions and challenges of memristive IMC-based general-purpose machine learning are proposed from a cross-level design perspective.