Journal Article

An in-memory computing architecture based on two-dimensional semiconductors for multiply-accumulate operations

Ma, Shunli, Sun, Qingqing, Chen, Lin, Wang, Yin, Sun, Zhengzong, Xie, Yufeng, Wan, Jing, Zhou, Peng, Zhu, Hao, Tang, Hongwei, Xu, Zihan, Bao, Wenzhong, Zhang, David Wei, Chen, Xinyu

2021

Overview

In-memory computing may enable multiply-accumulate (MAC) operations, which are the primary calculations used in artificial intelligence (AI). Performing MAC operations with high capacity in a small area with high energy efficiency remains a challenge. In this work, we propose a circuit architecture that integrates monolayer MoS 2 transistors in a two-transistor–one-capacitor (2T-1C) configuration. In this structure, the memory portion is similar to a 1T-1C Dynamic Random Access Memory (DRAM) so that theoretically the cycling endurance and erase/write speed inherit the merits of DRAM. Besides, the ultralow leakage current of the MoS 2 transistor enables the storage of multi-level voltages on the capacitor with a long retention time. The electrical characteristics of a single MoS 2 transistor also allow analog computation by multiplying the drain voltage by the stored voltage on the capacitor. The sum-of-product is then obtained by converging the currents from multiple 2T-1C units. Based on our experiment results, a neural network is ex-situ trained for image recognition with 90.3% accuracy. In the future, such 2T-1C units can potentially be integrated into three-dimensional (3D) circuits with dense logic and memory layers for low power in-situ training of neural networks in hardware. In standard computing architectures, memory and logic circuits are separated, a feature that slows matrix operations vital to deep learning algorithms. Here, the authors present an alternate in-memory architecture and demonstrate a feasible approach for analog matrix multiplication.

• Publisher: Nature Publishing Group UK,Nature Publishing Group,Nature Portfolio
• Subject: 639/925/357/1018 / 639/925/927/1007 / 639/925/929/115 / Algorithms / Artificial intelligence / Capacitors / Computation / Computer architecture / Deep learning / Dynamic random access memory / Electric potential / Energy conversion efficiency / Energy efficiency / Humanities and Social Sciences / Leakage current / Learning algorithms / Logic circuits / Machine learning / Molybdenum disulfide / multidisciplinary / Multiplication / Neural networks / Object recognition / Random access memory / Retention time / Science / Science (multidisciplinary) / Semiconductor devices / Transistors / Voltage