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NeuroRadar: A Neuromorphic Radar Sensor for Low-Power IoT Systems

Authors:

Timothy Woodford,

Xinyu ZhangAuthors Info & Claims

SenSys '23: Proceedings of the 21st ACM Conference on Embedded Networked Sensor Systems

Pages 223 - 236

https://doi.org/10.1145/3625687.3625788

Published: 26 April 2024 Publication History

Abstract

Radar sensors have recently been explored in the industrial and consumer Internet of Things (IoT). However, such applications often require self-sustainable or untethered operations, which are at odds with the high power consumption of radar. This paper proposes NeuroRadar, a neuromorphic radar sensor, to achieve low-power wireless sensing. NeuroRadar jointly optimizes the analog hardware and the computation model, in order to mimic the highly efficient biological sensing and neural processing system. NeuroRadar features a highly simplified radar front end, which eliminates the power-hungry components in conventional radars. It directly "encodes" ambient motion into spiking signals, which can be processed using spiking neural networks running on energy-efficient neuromorphic computing platforms. We have prototyped NeuroRadar and evaluated its performance in two use cases: gesture sensing and localization. Our experiments demonstrate that NeuroRadar can achieve high sensing accuracy, at orders of magnitude lower power consumption compared with traditional radar.

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Cited By

Jain IMm SBharadia DShu YLiu JTan RHe YChen J(2024)CommRad: Context-Aware Sensing-Driven Millimeter-Wave NetworksProceedings of the 22nd ACM Conference on Embedded Networked Sensor Systems10.1145/3666025.3699363(633-646)Online publication date: 4-Nov-2024
https://dl.acm.org/doi/10.1145/3666025.3699363
Zheng KZhao WWoodford TZhao RZhang XHua YOkoshi TKo JLiKamWa R(2024)Enhancing mmWave Radar Sensing Using a Phased-MIMO ArchitectureProceedings of the 22nd Annual International Conference on Mobile Systems, Applications and Services10.1145/3643832.3661865(56-69)Online publication date: 3-Jun-2024
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Ni TSun ZHan MXie YLan GLi ZGu TXu WPapavassiliou SSchmid S(2024)REHSense: Towards Battery-Free Wireless Sensing via Radio Frequency Energy HarvestingProceedings of the Twenty-fifth International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing10.1145/3641512.3686388(211-220)Online publication date: 14-Oct-2024
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Index Terms

NeuroRadar: A Neuromorphic Radar Sensor for Low-Power IoT Systems
1. Human-centered computing
  1. Ubiquitous and mobile computing
    1. Ubiquitous and mobile computing systems and tools
    2. Ubiquitous and mobile computing theory, concepts and paradigms

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cover image ACM Conferences

SenSys '23: Proceedings of the 21st ACM Conference on Embedded Networked Sensor Systems

November 2023

574 pages

ISBN:9798400704147

DOI:10.1145/3625687

General Chair:
Rasit Eskicioglu,
Program Chair:
Polly Huang,
Program Co-chair:
Neal Patwari

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Published: 26 April 2024

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SenSys '23: 21st ACM Conference on Embedded Networked Sensor Systems

November 12 - 17, 2023

Istanbul, Turkiye

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https://dl.acm.org/doi/10.1145/3666025.3699363
Zheng KZhao WWoodford TZhao RZhang XHua YOkoshi TKo JLiKamWa R(2024)Enhancing mmWave Radar Sensing Using a Phased-MIMO ArchitectureProceedings of the 22nd Annual International Conference on Mobile Systems, Applications and Services10.1145/3643832.3661865(56-69)Online publication date: 3-Jun-2024
https://dl.acm.org/doi/10.1145/3643832.3661865
Ni TSun ZHan MXie YLan GLi ZGu TXu WPapavassiliou SSchmid S(2024)REHSense: Towards Battery-Free Wireless Sensing via Radio Frequency Energy HarvestingProceedings of the Twenty-fifth International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing10.1145/3641512.3686388(211-220)Online publication date: 14-Oct-2024
https://dl.acm.org/doi/10.1145/3641512.3686388
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Ciattaglia GDe Santis ASenigagliesi LRaimondi MNocera AGambi ESpinsante S(2024)The Impact of Automotive Radar Configuration on Power Consumption: The Case of TI AWR18432024 IEEE International Workshop on Metrology for Automotive (MetroAutomotive)10.1109/MetroAutomotive61329.2024.10615722(29-34)Online publication date: 26-Jun-2024
https://doi.org/10.1109/MetroAutomotive61329.2024.10615722

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