DOI: https://doi.org/10.29363/nanoge.neumatdecas.2023.025
Publication date: 9th January 2023
Reservoir computing is noted for its low-training-cost capability in temporal signals processing [1,2]. Thus, hardware implementations tailored to requirements in reservoir computing would profoundly improve data processing in edge computing where the energy budget and bandwidth is strictly limited. In this work, we report a Hf0.5Zr0.5O2-based three-terminal ferroelectric memcapacitor (FMC) with nonideal depolarization dynamics for multi-sensory information processing physical reservoir computing (RC) system. Our RC system is verified by standard benchmarking tasks with high accuracy, and the power consumption (~2.4 nW per reservoir state) outperform most hardware and software-based RC systems. Furthermore, such physical RCs are capable of recognizing sensory information including graphic, acoustic, electrophysiological, and mechanic modalities. As proof-of-concept, a touchless user interface (TUI) for virtual shopping is demonstrated using FMC-based RC systems which can identify purchasing operations based on acoustic and electrophysiological commands. Our design would shed light on the development of high energy-efficiency edge computing apparatus.
This work was supported by the National Key Research and Development Program of China (Grant nos. 2021YFA1202600 and 2021YFA0715600), National Natural Science Foundation of China (Grant nos. 62174082, 62074075, 51861145202, and 61861166001), and in part by Natural Science Foundation of Jiangsu Province (Grant no. BK20211507).