Low-Complexity Fixed-Point Convolutional Neural Networks For Automatic Target Recognition

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 984X reduction in terms of storage requirements and 71X reduction in terms of computational complexity compared to state-of-the-art convolutional neural networks for automatic target recognition (ATR), while maintaining a classification accuracy of >99% on the MSTAR dataset.