Energy-Efficient Neuromorphic Architectures for Nuclear Radiation Detection Applications

Canales-Verdial, Jorge I.; Wagner, Jamison R.; Schmucker, Landon A.; Wetzel, Mark; Proctor, Philippe; Carson, Merlin; Meng, Jian; Withers, Nathan J.; Harris, Charles Thomas; Nogan, John J.; Webb, Denise B.; Hecht, Adam A.; Teuscher, Christof; Osiński, Marek; Zarkesh-Ha, Payman

doi:10.3390/s24072144

Title: Energy-Efficient Neuromorphic Architectures for Nuclear Radiation Detection Applications

Journal Article · 27 March 2024 · Sensors

DOI: https://doi.org/10.3390/s24072144 · OSTI ID:2469629

Canales-Verdial, Jorge I. ^[1]; Wagner, Jamison R. ^[1];

^[1]; Wetzel, Mark ^[1]; Proctor, Philippe ^[2];

^[2]; Meng, Jian ^[2]; Withers, Nathan J. ^[1];

^[3]; Nogan, John J. ^[3]; Webb, Denise B. ^[3]; Hecht, Adam A. ^[1]; Teuscher, Christof ^[2]; Osiński, Marek ^[1];

^[1]

University of New Mexico, Albuquerque, NM (United States)
Portland State University, Portland, OR (United States)
Center for Integrated Nanotechnologies, Albuquerque, NM (United States)

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.

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Research Organization:: Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)

Sponsoring Organization:: Defense Threat Reduction Agency (DTRA); USDOE Office of Science (SC), Basic Energy Sciences (BES). Scientific User Facilities (SUF)

Grant/Contract Number:: AC52-06NA25396; NA0003525

OSTI ID:: 2469629

Journal Information:: Sensors, Journal Name: Sensors Journal Issue: 7 Vol. 24; ISSN 1424-8220

Publisher:: MDPI AGCopyright Statement

Country of Publication:: United States

Language:: English

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46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND TECHNOLOGY
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engineering
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Title: Energy-Efficient Neuromorphic Architectures for Nuclear Radiation Detection Applications

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