| 具有感存算一体化的新型神经形态视觉传感器 |
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| 引用本文： | 廖付友, 柴扬. 具有感存算一体化的新型神经形态视觉传感器[J]. 物理, 2021, 50(6): 378-384. DOI: 10.7693/wl20210603 |
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| 作者姓名： | 廖付友 柴扬 |
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| 作者单位： | (1 香港理工大学深圳研究院 深圳 518063);(2 香港理工大学应用物理系 香港 999077) |
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| 基金项目： | 国家自然科学基金(批准号：61851402)、香港研究资助局(批准号：15205619)和深圳科技创新委员会(批准号：JCYJ20180507183424383)资助项目 |
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| 摘 要： | 传统的数字图像处理系统包括图像传感器与图像处理单元，二者在物理空间上分离，图像信息在其间的传输造成了延时与能耗。此外，数字图像传感器基于“帧”的工作原理，可能丢失一些重要信息，或者造成数据冗余。人类视觉系统提供了一种高效并行的信息处理方式。神经形态视觉传感器能够模拟人类视网膜的功能，同时具备感知光信号、存储信号和进行信息预处理的功能。这类感存算一体化的神经形态视觉传感器简化了人工视觉系统的电路复杂性，提升了信息处理效率，节省了系统功耗。文章总结了传统的数字图像传感器存在的问题，介绍了几种重要的人工神经网络，讨论了新型神经形态视觉传感器的研究进展和存在的问题。
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| 关 键 词： | 图像传感器 神经形态视觉传感器 人工神经网络 感存算一体化 |
| 收稿时间： | 2020-07-29 |
Neuromorphic vision sensors based on an in-sensor computing paradigm |
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| LIAO Fu-You, CHAI Yang. Neuromorphic vision sensors based on an in-sensor computing paradigm[J]. PHYSICS, 2021, 50(6): 378-384. DOI: 10.7693/wl20210603 |
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| Authors: | LIAO Fu-You CHAI Yang |
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| Affiliation: | (1 Shenzhen Research Institute,The Hong Kong Polytechnic University,Shenzhen 518063,China);(2 Department of Applied Physics,The Hong Kong Polytechnic University,Hong Kong 999077,China) |
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| Abstract: | Conventional digital image systems include both image sensors and image processing units, which are physically separated. The data transfer between them gives rise to time delay and high-power consumption. In addition, the digital imaging system works through a framebased operation, which causes the loss of important information or results in data redundancy. Our human visual system offers an efficient and parallel information processing method. Neuromorphic vision sensors can emulate the functions of the human retina for sensing light signals, storing the signals, and performing information preprocessing. This paradigm greatly simplifies the circuit complexity of the artificial vision system, improves the efficiency of information processing, and reduces system power consumption. In this work, we summarize the problems of conventional digital image sensors, introduce a few important artificial neural networks, and discuss the research progress and existing problems of emerging neuromorphic vision sensors. |
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| Keywords: | image sensor neuromorphic vision sensor artificial neural network in-sensor computing |
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