Abstract
Memory performance is a key bottleneck for deep learning systems. Binarization of both activations and weights is one promising approach that can best scale to realize the highest energy efficient system using the lowest possible precision. In this paper, we utilize and analyze the binarized neural network in doing human detection on infrared images. Our results show comparable algorithmic performance of binarized versus 32bit floating-point networks, with the added benefit of greatly simplified computation and reduced memory overhead. In addition, we present a system architecture designed specifically for computation using binary representation that achieves at least 4× speedup and the energy is improved by three orders of magnitude over GPU.
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Acknowledgments
This research was developed with funding from the Defense Advanced Research Projects Agency (DARPA), the Air Force Research Laboratory (AFRL), and NSF (#1526399). The views, opinions and/or findings expressed are those of the authors and should not be interpreted as representing the official views or policies of the Department of Defense or the U.S. Government.
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Kung, J., Zhang, D., van der Wal, G. et al. Efficient Object Detection Using Embedded Binarized Neural Networks. J Sign Process Syst 90, 877–890 (2018). https://doi.org/10.1007/s11265-017-1255-5
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DOI: https://doi.org/10.1007/s11265-017-1255-5