Efficient Object Detection Using Embedded Binarized Neural Networks

Efficient Object Detection Using Embedded Binarized Neural Networks 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Signal Processing Systems Springer Journals

Efficient Object Detection Using Embedded Binarized Neural Networks

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Publisher
Springer Journals
Copyright
Copyright © 2017 by Springer Science+Business Media New York
Subject
Engineering; Signal,Image and Speech Processing; Circuits and Systems; Electrical Engineering; Image Processing and Computer Vision; Pattern Recognition; Computer Imaging, Vision, Pattern Recognition and Graphics
ISSN
1939-8018
eISSN
1939-8115
D.O.I.
10.1007/s11265-017-1255-5
Publisher site
See Article on Publisher Site

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.

Journal

Journal of Signal Processing SystemsSpringer Journals

Published: Jun 8, 2017

References

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