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Lightweight, Deep RNNs for Radar Classification

Published: 13 November 2019 Publication History

Abstract

We demonstrate Multi-Scale, Cascaded RNN (MSC-RNN)1, an energy-efficient recurrent neural network for real-time micro-power radar classification. Its two-tier architecture is jointly trained to reject clutter and discriminate displacing sources at different time-scales, with a lighter lower tier running continuously and a heavier upper tier invoked infrequently on an on-demand basis. It offers for single microcontroller devices a better trade-off in accuracy and efficiency, as well as in clutter suppression and detectability, over competitive shallow and deep alternatives.

References

[1]
[n. d.]. CMSIS-DSP Software Library. http://www.keil.com/pack/doc/CMSIS/DSP/html/index.html.
[2]
Juan P. Bello, Claudio Silva, et al. 2019. SONYC: A system for monitoring, analyzing, and mitigating urban noise pollution. CACM 62, 2 (Jan. 2019), 68--77.
[3]
Don Dennis, Chirag Pabbaraju, et al. 2018. Multiple instance learning for efficient sequential data classification on resource-constrained devices. In NIPS. Curran Associates, Inc, 10975--10986.
[4]
Sepp Hochreiter and Jürgen Schmidhuber. 1997. Long short-term memory. Neural computation 9, 8 (1997), 1735--1780.
[5]
Aditya Kusupati, Manish Singh, et al. 2018. FastGRNN: A fast, accurate, stable and tiny kilobyte sized gated recurrent neural network. In NIPS. Curran Associates, Inc., 9030--9041.
[6]
Dhrubojyoti Roy, Christopher Morse, et al. 2017. Cross-environmentally robust intruder discrimination in radar motes. In MASS. IEEE, 426--434.
[7]
Dhrubojyoti Roy, Sangeeta Srivastava, et al. 2019. One size does not fit all: Multi-scale, cascaded RNNs for radar classification. In BuildSys. ACM, to appear.
[8]
Oliver Shih and Anthony Rowe. 2015. Occupancy estimation using ultrasonic chirps. In ICCPS. ACM, 149--158.
[9]
The Samraksh Company. [n. d.]. NOW with eMote. https://goo.gl/C4CCv4.

Cited By

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  • (2021)One Size Does Not Fit AllACM Transactions on Sensor Networks10.1145/343995717:2(1-27)Online publication date: 23-Jan-2021
  • (2021)Deep Incremental RNN for Learning Sequential Data: A Lyapunov Stable Dynamical System2021 IEEE International Conference on Data Mining (ICDM)10.1109/ICDM51629.2021.00108(966-975)Online publication date: Dec-2021

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BuildSys '19: Proceedings of the 6th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation
November 2019
413 pages
ISBN:9781450370059
DOI:10.1145/3360322
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 November 2019

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BuildSys '19 Paper Acceptance Rate 40 of 131 submissions, 31%;
Overall Acceptance Rate 148 of 500 submissions, 30%

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Cited By

View all
  • (2021)One Size Does Not Fit AllACM Transactions on Sensor Networks10.1145/343995717:2(1-27)Online publication date: 23-Jan-2021
  • (2021)Deep Incremental RNN for Learning Sequential Data: A Lyapunov Stable Dynamical System2021 IEEE International Conference on Data Mining (ICDM)10.1109/ICDM51629.2021.00108(966-975)Online publication date: Dec-2021

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