A PYNQ-based Framework for Rapid CNN Prototyping
File(s)main.pdf (117.15 KB)
Accepted version
Author(s)
Wang, Erwei
Davis, JJ
Cheung, Peter
Type
Conference Paper
Abstract
This work presents a self-contained and modifiable framework for fast and easy convolutional neural network prototyping on the Xilinx PYNQ platform. With a Python-based programming interface, the framework combines the convenience of high-level abstraction with the speed of optimised FPGA implementation. Our work is freely available on GitHub for the community to use and build upon.
Date Issued
2018-09-10
Date Acceptance
2018-03-06
Citation
2018, pp.223-223
ISBN
978-1-5386-5522-1
Publisher
IEEE
Start Page
223
End Page
223
Copyright Statement
© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Sponsor
Engineering & Physical Science Research Council (E
Engineering & Physical Science Research Council (EPSRC)
Grant Number
11908 (EP/K034448/1)
EP/P010040/1
Source
IEEE Symposium on Field-programmable Custom Computing Machines (FCCM) 2018
Publication Status
Published
Start Date
2018-04-29
Finish Date
2018-05-01
Coverage Spatial
Boulder, CO, USA
Date Publish Online
2018-09-10