Multiframe-integrated, in-sensor computing using persistent photoconductivity

IF 4.8 4区 物理与天体物理 Q2 PHYSICS, CONDENSED MATTER
Xiaoyong Jiang, Minrui Ye, Yunhai Li, Xiao Fu, Tangxin Li, Qixiao Zhao, Jinjin Wang, Tao Zhang, Jinshui Miao, Zengguang Cheng
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引用次数: 0

Abstract

The utilization of processing capabilities within the detector holds significant promise in addressing energy consumption and latency challenges. Especially in the context of dynamic motion recognition tasks, where substantial data transfers are necessitated by the generation of extensive information and the need for frame-by-frame analysis. Herein, we present a novel approach for dynamic motion recognition, leveraging a spatial-temporal in-sensor computing system rooted in multiframe integration by employing photodetector. Our approach introduced a retinomorphic MoS2 photodetector device for motion detection and analysis. The device enables the generation of informative final states, nonlinearly embedding both past and present frames. Subsequent multiply-accumulate (MAC) calculations are efficiently performed as the classifier. When evaluating our devices for target detection and direction classification, we achieved an impressive recognition accuracy of 93.5%. By eliminating the need for frame-by-frame analysis, our system not only achieves high precision but also facilitates energy-efficient in-sensor computing.
利用持久光电导技术实现多帧集成传感器内计算
利用检测器内部的处理能力在解决能耗和延迟问题方面大有可为。特别是在动态运动识别任务中,由于需要生成大量信息并进行逐帧分析,因此必须传输大量数据。在此,我们提出了一种新颖的动态运动识别方法,该方法通过采用光电探测器,利用植根于多帧集成的时空传感器内计算系统。我们的方法引入了一种视网膜形态的 MoS2 光电探测器设备,用于运动检测和分析。该装置可生成信息丰富的最终状态,非线性地嵌入过去和现在的帧。随后的乘法累加(MAC)计算可作为分类器有效执行。在对我们的设备进行目标检测和方向分类评估时,我们取得了令人印象深刻的 93.5% 的识别准确率。通过消除逐帧分析的需要,我们的系统不仅实现了高精度,还促进了高能效的传感器内计算。
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来源期刊
Journal of Semiconductors
Journal of Semiconductors PHYSICS, CONDENSED MATTER-
CiteScore
6.70
自引率
9.80%
发文量
119
期刊介绍: Journal of Semiconductors publishes articles that emphasize semiconductor physics, materials, devices, circuits, and related technology.
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