Performance Optimization Study of the Neuromorphic Radiation Anomaly Detector

James Ghawaly; Aaron Young; Andrew Nicholson; Brett Witherspoon; Nick Prins; Mathew Swinney; Cihangir Celik; Catherine Schuman; Karan Patel

doi:10.1145/3589737.3605980

Performance Optimization Study of the Neuromorphic Radiation Anomaly Detector

James Ghawaly, Aaron Young, Andrew Nicholson, Brett Witherspoon, Nick Prins, Mathew Swinney, Cihangir Celik, Catherine Schuman, Karan Patel

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Scopus citations

Abstract

This work reports on new results and insights from the optimization of spiking neural networks developed for gamma-ray radiation anomaly detection. Our previous paper introduced the first known neuromorphic algorithm for this application, demonstrating promising results and insights into optimal hyperparameter selection - particularly in the choice of data input encodings. Since the first paper, we have tested the algorithms on new datasets to investigate transferability from one background radiation environment to another. We have also performed a new hyperparameter optimization experiment with this new dataset to investigate the impact of new radiation data formatting techniques, the inclusion or neuronal temporality, and neuron charge leakage. This paper provides an overview and discussion of the results from this study.

Original language	English
Title of host publication	ICONS 2023 - Proceedings of International Conference on Neuromorphic Systems 2023
Publisher	Association for Computing Machinery
ISBN (Electronic)	9798400701757
DOIs	https://doi.org/10.1145/3589737.3605980
State	Published - Aug 1 2023
Event	2023 International Conference on Neuromorphic Systems, ICONS 2023 - Santa Fe, United States Duration: Aug 1 2023 → Aug 3 2023

Publication series

Name	ACM International Conference Proceeding Series

Conference

Conference	2023 International Conference on Neuromorphic Systems, ICONS 2023
Country/Territory	United States
City	Santa Fe
Period	08/1/23 → 08/3/23

Funding

Support for DOI 10.13139/ORNLNCCS/1597414 dataset is provided by the U.S. Department of Energy, project Modeling Urban Scenarios & Experiments (MUSE) under Contract DE-AC05-00OR22725. Project Modeling Urban Scenarios & Experiments (MUSE) used resources of the Oak Ridge Leadership Computing Facility at Oak Ridge National Laboratory, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DEAC05- 00OR22725. The authors would like to thank the US Department of Energy's National Nuclear Security Administration, Office of Defense Nuclear Nonproliferation Research and Development for funding to support this work. This manuscript has been authored by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan). This research used resources of the Experimental Computing Laboratory (ExCL) at the Oak Ridge National Laboratory, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC05-00OR22725 This research used resources of the Experimental Computing Laboratory (ExCL) at the Oak Ridge National Laboratory, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC05-00OR22725 The authors would like to thank the US Department of Energy’s National Nuclear Security Administration, Office of Defense Nuclear Nonproliferation Research and Development for funding to support this work. Support for DOI 10.13139/ORNLNCCS/1597414 dataset is provided by the U.S. Department of Energy, project Modeling Urban Scenarios & Experiments (MUSE) under Contract DE-AC05-00OR22725. Project Modeling Urban Scenarios & Experiments (MUSE) used resources of the Oak Ridge Leadership Computing Facility at Oak Ridge National Laboratory, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DEAC05-00OR22725.

Keywords

anomaly detection
bio-inspired computing
evolutionary optimization
neuromorphic computing
radiation detection
spiking neural networks

Access to Document

10.1145/3589737.3605980

Cite this

Ghawaly, J., Young, A., Nicholson, A., Witherspoon, B., Prins, N., Swinney, M., Celik, C., Schuman, C., & Patel, K. (2023). Performance Optimization Study of the Neuromorphic Radiation Anomaly Detector. In ICONS 2023 - Proceedings of International Conference on Neuromorphic Systems 2023 Article 13 (ACM International Conference Proceeding Series). Association for Computing Machinery. https://doi.org/10.1145/3589737.3605980

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abstract = "This work reports on new results and insights from the optimization of spiking neural networks developed for gamma-ray radiation anomaly detection. Our previous paper introduced the first known neuromorphic algorithm for this application, demonstrating promising results and insights into optimal hyperparameter selection - particularly in the choice of data input encodings. Since the first paper, we have tested the algorithms on new datasets to investigate transferability from one background radiation environment to another. We have also performed a new hyperparameter optimization experiment with this new dataset to investigate the impact of new radiation data formatting techniques, the inclusion or neuronal temporality, and neuron charge leakage. This paper provides an overview and discussion of the results from this study.",

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Ghawaly, J, Young, A , Nicholson, A , Witherspoon, B, Prins, N, Swinney, M , Celik, C, Schuman, C & Patel, K 2023, Performance Optimization Study of the Neuromorphic Radiation Anomaly Detector. in ICONS 2023 - Proceedings of International Conference on Neuromorphic Systems 2023., 13, ACM International Conference Proceeding Series, Association for Computing Machinery, 2023 International Conference on Neuromorphic Systems, ICONS 2023, Santa Fe, United States, 08/1/23. https://doi.org/10.1145/3589737.3605980

Performance Optimization Study of the Neuromorphic Radiation Anomaly Detector. / Ghawaly, James; Young, Aaron ; Nicholson, Andrew et al.
ICONS 2023 - Proceedings of International Conference on Neuromorphic Systems 2023. Association for Computing Machinery, 2023. 13 (ACM International Conference Proceeding Series).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Celik, Cihangir

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AB - This work reports on new results and insights from the optimization of spiking neural networks developed for gamma-ray radiation anomaly detection. Our previous paper introduced the first known neuromorphic algorithm for this application, demonstrating promising results and insights into optimal hyperparameter selection - particularly in the choice of data input encodings. Since the first paper, we have tested the algorithms on new datasets to investigate transferability from one background radiation environment to another. We have also performed a new hyperparameter optimization experiment with this new dataset to investigate the impact of new radiation data formatting techniques, the inclusion or neuronal temporality, and neuron charge leakage. This paper provides an overview and discussion of the results from this study.

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Performance Optimization Study of the Neuromorphic Radiation Anomaly Detector

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