@inproceedings{e02db1f816cc49ab9a1c6fe636653078,
title = "Low-Complexity Fixed-Point Convolutional Neural Networks for Automatic Target Recognition",
abstract = "There has been growing interest in developing neural network based automatic target recognition systems for synthetic aperture radar applications. However, these networks are typically complex in terms of storage and computation which inhibits their deployment in the field, where such resources are heavily constrained. In order to bring the cost of implementing these networks down, we develop a set of compact network architectures and train them in fixed-point. Our proposed method achieves an overall 984 reduction in terms of storage requirements and 71 × reduction in terms of computational complexity compared to state-of-the-art con-volutional neural networks for automatic target recognition (ATR), while maintaining a classification accuracy of > 99% on the MSTAR dataset.",
keywords = "automatic target recognition, deep learning, neural networks, quantization, synthetic aperture radar",
author = "Hassan Dbouk and Hanfei Geng and Vineyard, {Craig M.} and Shanbhag, {Naresh R.}",
note = "Funding Information: This work is supported by a grant from Sandia National Laboratories and DARPA FRANC Program. Publisher Copyright: {\textcopyright} 2020 IEEE.; 2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 ; Conference date: 04-05-2020 Through 08-05-2020",
year = "2020",
month = may,
doi = "10.1109/ICASSP40776.2020.9054094",
language = "English (US)",
series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1598--1602",
booktitle = "2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Proceedings",
address = "United States",
}