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30 May 2022 Spiking neural approaches for SAR ATR
Craig M. Vineyard, William M. Severa, Aaron Hill, James B. Aimone, Esteban J. Guillen, Javier Zazueta, Ryan Dellana
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Proceedings Volume PC12096, Automatic Target Recognition XXXII; PC1209603 (2022) https://doi.org/10.1117/12.2618898
Event: SPIE Defense + Commercial Sensing, 2022, Orlando, Florida, United States
Abstract
Spiking neural networks (SNNs) extend upon traditional artificial neural networks (ANNs) by incorporating increased biological fidelity. For example, this includes features such as event-driven operation, sparsity, spatial/temporal functionality, parallelism, and collocating processing and memory. These features can translate into efficient computing hardware design, and consequently SNNs offer potential advantages for SAR ATR. Here we provide a wide exploration into several SNN approaches, both for algorithms and computing hardware. Using the MSTAR and SAMPLE benchmark datasets, we develop SAR ATR networks comparing SNN computational complexity tradeoffs and analyzing how respective neuromorphic architectural choices impact spiking neural ATR performance.
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Craig M. Vineyard, William M. Severa, Aaron Hill, James B. Aimone, Esteban J. Guillen, Javier Zazueta, and Ryan Dellana "Spiking neural approaches for SAR ATR", Proc. SPIE PC12096, Automatic Target Recognition XXXII, PC1209603 (30 May 2022); https://doi.org/10.1117/12.2618898
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KEYWORDS
Synthetic aperture radar
Neurons
Neural networks
Artificial neural networks
Brain
Computer architecture
Convolutional neural networks
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