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Event-Driven Sensing and Embedded Neuromorphic Platforms for Gamma Radiation Monitoring

Published: 12 June 2024 Publication History

Abstract

This work will present an embedded neuromorphic platform developed for long-term unintended gamma radiation monitoring. We describe the hardware architecture and supporting software developed to demonstrate neuromorphic computing for applications where ultra-low power and always-on sensing are required. This is followed by a discussion of our current work on an improved platform that integrates both event-driven vision and gamma-ray spectroscopy sensors for nuclear safeguards applications. Finally, future research directions toward event-driven sampling techniques to integrate analog-to-information reduction into the sensing electronics are proposed.

References

[1]
Semiconductor Research Corporation. 2021. The Decadal Plan for Semiconductors.
[2]
James Ghawaly, Aaron Young, Dan Archer, Nick Prins, Brett Witherspoon, and Catherine Schuman. 2022. A neuromorphic algorithm for radiation anomaly detection. In Proceedings of the International Conference on Neuromorphic Systems 2022. 1–6.
[3]
James Ghawaly, Aaron Young, Andrew Nicholson, Brett Witherspoon, Nick Prins, Mathew Swinney, Cihangir Celik, Catherine Schuman, and Karan Patel. 2023. Performance Optimization Study of the Neuromorphic Radiation Anomaly Detector. In Proceedings of the 2023 International Conference on Neuromorphic Systems. 1–7.
[4]
Hyeryung Jang, Osvaldo Simeone, Brian Gardner, and Andre Gruning. 2019. An introduction to probabilistic spiking neural networks: Probabilistic models, learning rules, and applications. IEEE Signal Processing Magazine 36, 6 (2019), 64–77.
[5]
Glenn F Knoll. 2010. Radiation detection and measurement. John Wiley & Sons.
[6]
Aurel A Lazar. 2004. Time encoding with an integrate-and-fire neuron with a refractory period. Neurocomputing 58 (2004), 53–58.
[7]
Aurel A Lazar. 2005. Multichannel time encoding with integrate-and-fire neurons. Neurocomputing 65 (2005), 401–407.
[8]
Aurel A Lazar and László T Tóth. 2004. Perfect recovery and sensitivity analysis of time encoded bandlimited signals. IEEE Transactions on Circuits and Systems I: Regular Papers 51, 10 (2004), 2060–2073.
[9]
Patrick Lichtensteiner, Christoph Posch, and T Delbruck. 2008. A 128x128 120dB 15μ s Latency Asynchronous Temporal Contrast Vision Sensor. IEEE Journal of Solid-State Circuits2 (2008), 566–576.
[10]
J Parker Mitchell, Catherine D Schuman, Robert M Patton, and Thomas E Potok. 2020. Caspian: A neuromorphic development platform. In Proceedings of the 2020 Annual Neuro-Inspired Computational Elements Workshop. 1–6.
[11]
J Parker Mitchell, Catherine D Schuman, and Thomas E Potok. 2020. A small, low cost event-driven architecture for spiking neural networks on fpgas. In International Conference on Neuromorphic Systems 2020. 1–4.
[12]
PW Nicholson and Ronald Nutt. 1975. Nuclear electronics. American Institute of Physics.
[13]
Shruti R. Kulkarni, Aaron Young, Prasanna Date, Narasinga Rao Miniskar, Jeffrey Vetter, Farah Fahim, Benjamin Parpillon, Jennet Dickinson, Nhan Tran, Jieun Yoo, 2023. On-sensor data filtering using neuromorphic computing for high energy physics experiments. In Proceedings of the 2023 International Conference on Neuromorphic Systems. 1–8.
[14]
Helmuth Spieler. 2003. Front-end electronics and signal processing. Proceedings of the First ICFA School at the ICFA Instrumentation Center in Morelia, AIP vol. 674, pp. 76-100 674 (2003).
[15]
Jieun Yoo, Jennet Dickinson, Morris Swartz, Giuseppe Di Guglielmo, Alice Bean, Douglas Berry, Manuel Blanco Valentin, Karri DiPetrillo, Farah Fahim, Lindsey Gray, 2023. Smart pixel sensors: towards on-sensor filtering of pixel clusters with deep learning. arXiv preprint arXiv:2310.02474 (2023).
[16]
Aaron R Young, Mark E Dean, James S Plank, and Garrett S Rose. 2019. A review of spiking neuromorphic hardware communication systems. IEEE Access 7 (2019), 135606–135620.

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cover image ACM Conferences
GLSVLSI '24: Proceedings of the Great Lakes Symposium on VLSI 2024
June 2024
797 pages
ISBN:9798400706059
DOI:10.1145/3649476
Publication rights licensed to ACM. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of the United States government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

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Publication History

Published: 12 June 2024

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Author Tags

  1. anomaly detection
  2. bio-inspired computing
  3. event-driven sampling
  4. neuromorphic computing
  5. radiation detection
  6. spiking neural networks

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GLSVLSI '24
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GLSVLSI '24: Great Lakes Symposium on VLSI 2024
June 12 - 14, 2024
FL, Clearwater, USA

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Overall Acceptance Rate 312 of 1,156 submissions, 27%

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