题目：Bioinspired in-sensor computing for artificial vision用于人工视觉的仿生传感器内置计算

时间：2024年4月18日（周四）10:30

地点：国际校区C1-b114

主讲人：柴扬Yang Chai 

Abstract

The visual scene in the physical world integrates multidimensional information (spatial, temporal, polarization, spectrum, etc) and typically displays unstructured characteristics. Conventional image sensors cannot process this multidimensional vision data, creating a need for vision sensors that can efficiently extract features from substantial multidimensional vision data. Vision sensors are able to transform the unstructured visual scene into featured information without relying on sophisticated algorithms and complex hardware.

In this talk, I will describe our team’s efforts towards bioinspired in-sensor computing for artificial vision. I will talk about the framework of the in-sensor computing and demonstrate a few vision sensors for different scenarios, including visual adaptation, motion perception, as well as event-driven vision sensors for spiking neural network.

摘要：物理世界中的视觉场景集成了多维信息（空间、时间、偏振、光谱等），通常表现出非结构化特征。传统的图像传感器无法处理这种多维视觉数据，因此需要视觉传感器，它能够实现从大量多维视觉数据中有效提取特征。视觉传感器能够将非结构化的视觉场景转换为特征信息，而无需依赖复杂的算法和复杂的硬件。

在本次报告中，我将介绍我们团队在人工视觉的仿生传感器内置计算方面的工作进展，包括传感器内置计算的框架，并演示一些用于不同场景的视觉传感器，包括视觉适应、运动感知以及用于脉冲神经网络的事件驱动视觉传感器。

Introduction

Prof. Yang Chai is the Associate Dean of the Faculty of Science, Director of Joint Research Center of Microelectronics of the Hong Kong Polytechnic University, Vice President of the Physical Society of Hong Kong, a member of The Hong Kong Young Academy of Sciences, an IEEE Distinguished Lecturer since 2016, the Vice Chair of IEEE EDS region 10, the Chair of IEEE EDS Nanotechnology Committee, and was the Chair of IEEE ED/SSC Hong Kong Chapter (2017-2019). His current research interest mainly focuses on emerging electronic devices.

柴扬教授，香港理工大学理学院副院长、微电子联合研究中心主任、香港物理学会副会长、香港青年科学院院士、IEEE特聘讲师（2016年起）、IEEE EDS第10区副主席、 2017-2019年，IEEE EDS纳米技术委员会主席，以及IEEE ED/SSC香港分会主席。近年来，他研究兴趣主要致力于新型电子器件。

References

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[6] Nature Electronics, 2022, 5, 483-484

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[9] Nature Nanotechnology, 2024, accepted

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