Programming memristor arrays with arbitrarily high precision for analog computing
Wenhao Song, Mingyi Rao, Yunning Li, Can Li, Ye Zhuo, Fuxi Cai, M Wu, Wenbo Yin, Li Zongze, Qiang Wei, Sangsoo Lee, Hengfang Zhu, Lei Gong, Mark Barnell, Qing Wu, Peter A. Beerel, Mike Shuo‐Wei Chen, Ning Ge, Miao Hu, Qiangfei Xia, J. Joshua Yang
Abstract
In-memory computing represents an effective method for modeling complex physical systems that are typically challenging for conventional computing architectures but has been hindered by issues such as reading noise and writing variability that restrict scalability, accuracy, and precision in high-performance computations. We propose and demonstrate a circuit architecture and programming protocol that converts the analog computing result to digital at the last step and enables low-precision analog devices to perform high-precision computing. We use a weighted sum of multiple devices to represent one number, in which subsequently programmed devices are used to compensate for preceding programming errors. With a memristor system-on-chip, we experimentally demonstrate high-precision solutions for multiple scientific computing tasks while maintaining a substantial power efficiency advantage over conventional digital approaches.