In-memory Learning with Analog Resistive Switching Memory: A Review and Perspective

In this article, we review the existing analog resistive switching memory (RSM) devices and their hardware technologies for in-memory learning, as well as their challenges and prospects. Since the characteristics of the devices are different for in-memory learning and digital memory applications, it is important to have an in-depth understanding across different layers from devices and circuits to architectures and algorithms. First, based on a top-down view from architecture to devices for analog computing, we define the main figures of merit (FoMs) and perform a comprehensive analysis of analog RSM hardware including the basic device characteristics, hardware algorithms, and the corresponding mapping methods for device arrays, as well as the architecture and circuit design considerations for neural networks. Second, we classify the FoMs of analog RSM devices into two levels. Level 1 FoMs are essential for achieving the functionality of a system (e.g., linearity, symmetry, dynamic range, level numbers, fluctuation, variability, and yield). Level 2 FoMs are those that make a functional system more efficient and reliable (e.g., area, operational voltage, energy consumption, speed, endurance, retention, and compatibility with back-end-of-line processing). By constructing a device-to-application simulation framework, we perform an in-depth analysis of how these FoMs influence in-memory learning and give a target list of the device requirements. Lastly, we evaluate the main FoMs of most existing devices with analog characteristics and review optimization methods from programming schemes to materials and device structures. The key challenges and prospects from the device to system level for analog RSM devices are discussed.

I. Introduction

Ever since Hinton [1] published his phenomenal article in 2007, renewed interest in artificial neural networks (ANNs) has contributed significantly to the progress of artificial intelligence (AI) and led to the explosive growth of deep learning. As one of the hottest research topics today, AI has generated widespread interest from both industry and academia, simultaneously causing remarkable social impact (e.g., Google’s AlphaGo [2]). Various tasks ranging from visual recognition, autonomous driving, voice assistance, chess playing, and many others have already been realized, and some AI applications have even achieved better cognitive accuracy than humans [3].