Optimizing CNN-based object detection algorithms on embedded FPGA platforms
File(s)
Accepted version
Author(s)
Zhao, R
Niu, X
Wu, Y
Luk, W
Liu, Q
Type
Conference Paper
Abstract
Algorithms based on Convolutional Neural Network (CNN) have recently been applied to object detection applications, greatly improving their performance. However, many devices intended for these algorithms have limited computation resources and strict power consumption constraints, and are not suitable for algorithms designed for GPU workstations. This paper presents a novel method to optimise CNNbased object detection algorithms targeting embedded FPGA platforms. Given parameterised CNN hardware modules, an optimisation flow takes network architectures and resource constraints as input, and tunes hardware parameters with algorithm-specific information to explore the design space and achieve high performance. The evaluation shows that our design model accuracy is above 85% and, with optimised configuration, our design can achieve 49.6 times speed-up compared with software implementation.
Date Issued
2017-03-31
Date Acceptance
2017-03-01
Citation
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2017, 10216, pp.255-267
ISBN
9783319562575
ISSN
0302-9743
Publisher
Springer
Start Page
255
End Page
267
Journal / Book Title
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume
10216
Copyright Statement
© Springer International Publishing AG 2017. The final publication is available at Springer via https://link.springer.com/chapter/10.1007%2F978-3-319-56258-2_22
Sponsor
Engineering & Physical Science Research Council (E
Commission of the European Communities
Engineering & Physical Science Research Council (E
Grant Number
PO 1553380
671653
516075101 (EP/N031768/1)
Source
13th International Symposium, ARC 2017
Subjects
08 Information And Computing Sciences
Artificial Intelligence & Image Processing
Publication Status
Published
Start Date
2017-04-03
Finish Date
2017-04-07
Coverage Spatial
Delft, The Netherlands