Towards closing the energy gap between HOG and CNN features for embedded vision
Name
1703.05853.pdf
Description
Accepted version
Size
1.63 MB
Format
Adobe PDF
Checksum (MD5)
5a3db4b17aed7509337f670a1363c9bd
Author(s)
Suleiman, Amr AbdulZahir
Chen, Yu-Hsin
Emer, Joel S
Sze, Vivienne
Date Issued
September 2017
Journal
2017 IEEE International Symposium on Circuits and Systems (ISCAS)
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Suleiman, Amr et al. "Towards closing the energy gap between HOG and CNN features for embedded vision." 2017 IEEE International Symposium on Circuits and Systems (ISCAS), May 2017, Baltimore, Maryland, Institute of Electrical and Electronics Engineers (IEEE), September 2017 © 2017 IEEE
Version
Author's final manuscript
Abstract
Computer vision enables a wide range of applications in robotics/drones, self-driving cars, smart Internet of Things, and portable/wearable electronics. For many of these applications, local embedded processing is preferred due to privacy and/or latency concerns. Accordingly, energy-efficient embedded vision hardware delivering real-time and robust performance is crucial. While deep learning is gaining popularity in several computer vision algorithms, a significant energy consumption difference exists compared to traditional hand-crafted approaches. In this paper, we provide an in-depth analysis of the computation, energy and accuracy trade-offs between learned features such as deep Convolutional Neural Networks (CNN) and hand-crafted features such as Histogram of Oriented Gradients (HOG). This analysis is supported by measurements from two chips that implement these algorithms. Our goal is to understand the source of the energy discrepancy between the two approaches and to provide insight about the potential areas where CNNs can be improved and eventually approach the energy-efficiency of HOG while maintaining its outstanding performance accuracy.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Microsystems Technology Laboratories
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/iscas.2017.8050341
