计算机科学
油藏计算
神经形态工程学
延迟(音频)
非阻塞I/O
任务(项目管理)
低延迟(资本市场)
人工智能
实时计算
嵌入式系统
人工神经网络
循环神经网络
工程类
计算机网络
电信
生物化学
化学
系统工程
催化作用
作者
Bingqi Cai,Tianyu Wang,Chen Wang,Qingqing Sun,David Wei Zhang,Lin Chen
出处
期刊:IEEE Electron Device Letters
[Institute of Electrical and Electronics Engineers]
日期:2024-02-05
卷期号:45 (4): 570-573
标识
DOI:10.1109/led.2024.3359775
摘要
In-sensor reservoir computing has recently gained considerable attention due to its highly efficient training process and advanced integration of sensing, storage, and processing functionalities. These advancements greatly enhance the machine vision capabilities by reducing data latency and energy overheads. However, the development of a highly efficient and low-cost in-sensor reservoir computing system remains a challenging task, primarily due to the lack of suitable materials and processes. In this paper, we present a simple ITO/NiO x /Au two-terminal photomemristor fabricated using the full physical vapor deposition (PVD) technique at room temperature without further treatment. This photomemristor leverages light-triggered dynamics to map input signals into a high-dimensional space and extract hidden information. As a proof of concept, we demonstrate an in-sensor reservoir computing system based on the photomemristor. Experimental results indicate that the system exhibits an impressive accuracy of 90.88% for image classification task and a low normalized root mean squared error (NRMSE) of 0.0082 for time-series prediction task. This work has complemented the wide spectrum of applications of NiO x in in-sensor neuromorphic computing.
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