Cryogenic in-memory computing using magnetic topological insulators

作者：Yuting Liu, Albert Lee, Kun Qian, Peng Zhang, Zhihua Xiao, Haoran He, Zheyu Ren, Shun Kong Cheung, Liu, Ruizi, Yaoyin Li, Xu Zhang, Zichao Ma, Zhao, Jianyuan, Zhao, Weiwei, Guoqiang Yu, Xin Wang, Junwei Liu, Zhongrui Wang, Kang L. Wang, Qiming Shao · 发表于：arXiv (Cornell University) · 年份：2022 · DOI：10.48550/arxiv.2209.09443 · 被引用次数：5 · 研究领域：Advanced Memory and Neural Computing、Neural Networks and Applications、Ferroelectric and Negative Capacitance Devices

Machine learning algorithms have been proven effective for essential quantum computation tasks such as quantum error correction and quantum control. Efficient hardware implementation of these algorithms at cryogenic temperatures is essential. Here, we utilize magnetic topological insulators as memristors (termed magnetic topological memristors) and introduce a cryogenic in-memory computing scheme based on the coexistence of the chiral edge state and the topological surface state. The memristive switching and reading of the giant anomalous Hall effect exhibit high energy efficiency, high stability, and low stochasticity. We achieve high accuracy in a proof-of-concept classification task using four magnetic topological memristors. Furthermore, our algorithm-level and circuit-level simulations of large-scale neural networks demonstrate software-level accuracy and lower energy consumption for image recognition and quantum state preparation compared with existing magnetic memristor and CMOS technologies. Our results not only showcase a new application of chiral edge states but also may inspire further topological quantum physics-based novel computing schemes.