Dr. Mark Plagge Profile

Dr. Mark Plagge

at Sandia National Labs.

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Proceedings Article | 12 April 2021 Presentation + Paper

Exploring characteristics of neural network architecture computation for enabling SAR ATR

Ryan Melzer, William Severa, Mark Plagge, Craig Vineyard

Proceedings Volume 11729, 1172909 (2021) https://doi.org/10.1117/12.2588006

KEYWORDS: Neural networks, Synthetic aperture radar, Computer architecture, Data modeling, Machine vision, Machine learning, Convolution, Computer vision technology, Computer simulations

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Neural network approaches have periodically been explored in the pursuit of high performing SAR ATR solutions. With deep neural networks (DNNs) now offering many state-of-the-art solutions to computer vision tasks, neural networks are once again being revisited for ATR processing. Here, we characterize and explore a suite of neural network architectural topologies. In doing so, we assess how different architectural approaches impact performance and consider the associated computational costs. This includes characterizing network depth, width, scale, connectivity patterns, as well as convolution layer optimizations. We have explored a suite of architectural topologies applied to both the canonical MSTAR dataset, as well as the more operationally realistic Synthetic and Measured Paired and Labeled Experiment (SAMPLE) dataset. The latter pairs high fidelity computational models of targets with actual measured SAR data. Effectively, this dataset offers the ability to train a DNN on simulated data and test the network performance on measured data. Not only does our in-depth architecture topology analysis offer insight into how different architectural approaches impact performance, but we also have trained DNNs attaining state-of-the-art performance on both datasets. Furthermore, beyond just accuracy, we also assess how efficiently an accelerator architecture executes these neural networks. Specifically, Using an analytical assessment tool, we forecast energy and latency for an edge TPU like architecture. Taken together, this tradespace exploration offers insight into the interplay of accuracy, energy, and latency for executing these networks.

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