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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