Selfputing: A 0.57 μW @ 15 fps Vision Chip with Self-powered In-Pixel Computing and In-Memory Computing for Visual Perception on the Edge

This paper presents an intelligent vision chip featuring self-powered in-pixel computing and in-memory computing (Selfputing) for distributed vision sensor nodes. The sensed light signal is processed with computing-in-pixel (CIP) scheme in the pixel array utilizing light-induced photocurrent to extract convolutional features. The output of CIP array is processed by SRAM-based charge-domain computing-in-memory (CIM) array to derive the image classification result. A power-managementunit driven by energy-harvesting photovoltaic (PV) cell is designed to power the CIP and CIM array. The Selfputing chip is fabricated in 180 nm CMOS process and consumes $0.57 \mu \mathrm{~W}$ power at a frame rate of 15 fps with 95.1% accuracy on MNIST dataset, achieving self-powered operation when driven by a PV cell with $1.26 \mu \mathrm{~W}$ harvesting power.

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Publication Details

Published
2024-09-09
DOI
https://doi.org/10.1109/esserc62670.2024.10719556
Citations
3
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
0.63

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article

Selfputing: A 0.57 μW @ 15 fps Vision Chip with Self-powered In-Pixel Computing and In-Memory Computing for Visual Perception on the Edge

Maimaiti Nazhamaiti, Haijin Su, Fei Qiao, Wanshou Jiang et al.
3 citations
Advanced Memory and Neural Computing
0.63
article

Selfputing: A 0.57 μW @ 15 fps Vision Chip with Self-powered In-Pixel Computing and In-Memory Computing for Visual Perception on the Edge

Maimaiti Nazhamaiti, Haijin Su, Fei Qiao, Wanshou Jiang, Qi Wei, Li Luo, Yutong Li, Xin Hong, Junkai Huang, Jinshui Miao, Bo Liu, Xiangjun Wang
article en
3 citations

Abstract

This paper presents an intelligent vision chip featuring self-powered in-pixel computing and in-memory computing (Selfputing) for distributed vision sensor nodes. The sensed light signal is processed with computing-in-pixel (CIP) scheme in the pixel array utilizing light-induced photocurrent to extract convolutional features. The output of CIP array is processed by SRAM-based charge-domain computing-in-memory (CIM) array to derive the image classification result. A power-managementunit driven by energy-harvesting photovoltaic (PV) cell is designed to power the CIP and CIM array. The Selfputing chip is fabricated in 180 nm CMOS process and consumes $0.57 \mu \mathrm{~W}$ power at a frame rate of 15 fps with 95.1% accuracy on MNIST dataset, achieving self-powered operation when driven by a PV cell with $1.26 \mu \mathrm{~W}$ harvesting power.

Jinan University (CN), Henan University of Science and Technology (CN), Beijing Jiaotong University (CN), University of Chinese Academy of Sciences (CN), Tsinghua University (CN)
National Natural Science Foundation of China, Research and Development
Affordable and clean energy
Openalex Percentile: Top 31%
Advanced Memory and Neural Computing
0.63
DOISourceOpenAlex
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