Multiply accumulate operations in memristor crossbar arrays for analog computing

• 作者: Jia Chen Jiancong Li Yi Li Xiangshui Miao
• 来源期刊: 半导体学报（英文版）
• 年，卷（期）: 2021,（1）
• 所属期刊栏目: REVIEWS
• 页码范围: 87-108
• 页数: 22页
• 语种: 英文
• DOI: 10.1088/1674-4926/42/1/013104

摘要

Memristors are now becoming a prominent candidate to serve as the building blocks of non-von Neumann in-memory computing architectures.By mapping analog numerical matrices into memristor crossbar arrays,efficient multiply accu-mulate operations can be performed in a massively parallel fashion using the physics mechanisms of Ohm's law and Kirchhoff's law.In this brief review,we present the recent progress in two niche applications:neural network accelerators and numerical computing units,mainly focusing on the advances in hardware demonstrations.The former one is regarded as soft computing since it can tolerant some degree of the device and array imperfections.The acceleration of multiple layer perceptrons,convolu-tional neural networks,generative adversarial networks,and long short-term memory neural networks are described.The lat-ter one is hard computing because the solving of numerical problems requires high-precision devices.Several breakthroughs in memristive equation solvers with improved computation accuracies are highlighted.Besides,other nonvolatile devices with the capability of analog computing are also briefly introduced.Finally,we conclude the review with discussions on the chal?lenges and opportunities for future research toward realizing memristive analog computing machines.