Toolflows for Mapping Convolutional Neural Networks on FPGAs: A Survey and Future Directions.
File(s)
Published version
Author(s)
Venieris, Stylianos I
Kouris, Alexandros
Bouganis, Christos-Savvas
Type
Journal Article
Abstract
n the past decade, Convolutional Neural Networks (CNNs) have demonstrated state-of-the-art performance
in various Artificial Intelligence tasks. To accelerate the experimentation and development of CNNs, several
software frameworks have been released, primarily targeting power-hungry CPUs and GPUs. In this context,
reconfigurable hardware in the form of FPGAs constitutes a potential alternative platform that can be integrated
in the existing deep learning ecosystem to provide a tunable balance between performance, power consumption
and programmability. In this paper, a survey of the existing CNN-to-FPGA toolflows is presented, comprising a
comparative study of their key characteristics which include the supported applications, architectural choices,
design space exploration methods and achieved performance. Moreover, major challenges and objectives
introduced by the latest trends in CNN algorithmic research are identified and presented. Finally, a uniform
evaluation methodology is proposed, aiming at the comprehensive, complete and in-depth evaluation of
CNN-to-FPGA toolflows.
in various Artificial Intelligence tasks. To accelerate the experimentation and development of CNNs, several
software frameworks have been released, primarily targeting power-hungry CPUs and GPUs. In this context,
reconfigurable hardware in the form of FPGAs constitutes a potential alternative platform that can be integrated
in the existing deep learning ecosystem to provide a tunable balance between performance, power consumption
and programmability. In this paper, a survey of the existing CNN-to-FPGA toolflows is presented, comprising a
comparative study of their key characteristics which include the supported applications, architectural choices,
design space exploration methods and achieved performance. Moreover, major challenges and objectives
introduced by the latest trends in CNN algorithmic research are identified and presented. Finally, a uniform
evaluation methodology is proposed, aiming at the comprehensive, complete and in-depth evaluation of
CNN-to-FPGA toolflows.
Date Issued
2018-05-01
Date Acceptance
2018-04-24
Citation
ACM Comput. Surv., 2018, 51 (3), pp.56:1-56:1
ISSN
0360-0300
Publisher
Association for Computing Machinery
Start Page
56:1
End Page
56:1
Journal / Book Title
ACM Comput. Surv.
Volume
51
Issue
3
Replaces
10044/1/57783
Copyright Statement
© 2018 Copyright is held by the owner/author(s). This work is licensed under a Creative Commons Attribution International 4.0 License (https://creativecommons.org/licenses/by/4.0/)
Subjects
cs.CV
cs.AR
cs.LG
08 Information And Computing Sciences
Information Systems
Publication Status
Published
Article Number
3