1. Energy-Efficient Neuromorphic Architectures for Nuclear Radiation Detection Applications
- Author
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Jorge I. Canales-Verdial, Jamison R. Wagner, Landon A. Schmucker, Mark Wetzel, Philippe Proctor, Merlin Carson, Jian Meng, Nathan J. Withers, Charles Thomas Harris, John J. Nogan, Denise B. Webb, Adam A. Hecht, Christof Teuscher, Marek Osiński, and Payman Zarkesh-Ha
- Subjects
neuromorphic computing ,memristor arrays ,radionuclide detection ,radioisotope classification ,source localization ,Chemical technology ,TP1-1185 - Abstract
A comprehensive analysis and simulation of two memristor-based neuromorphic architectures for nuclear radiation detection is presented. Both scalable architectures retrofit a locally competitive algorithm to solve overcomplete sparse approximation problems by harnessing memristor crossbar execution of vector–matrix multiplications. The proposed systems demonstrate excellent accuracy and throughput while consuming minimal energy for radionuclide detection. To ensure that the simulation results of our proposed hardware are realistic, the memristor parameters are chosen from our own fabricated memristor devices. Based on these results, we conclude that memristor-based computing is the preeminent technology for a radiation detection platform.
- Published
- 2024
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