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A PYNQ-based Framework for Rapid CNN Prototyping
main.pdf| Title: | A PYNQ-based Framework for Rapid CNN Prototyping |
| Authors: | Wang, E Davis, JJ Cheung, P |
| Item 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. |
| Issue Date: | 29-Apr-2018 |
| Date of Acceptance: | 6-Mar-2018 |
| URI: | http://hdl.handle.net/10044/1/57937 |
| Publisher: | IEEE |
| 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/Funder: | Engineering & Physical Science Research Council (E Engineering & Physical Science Research Council (EPSRC) |
| Funder's Grant Number: | 11908 (EP/K034448/1) EP/P010040/1 |
| Conference Name: | IEEE Symposium on Field-programmable Custom Computing Machines (FCCM) |
| Publication Status: | Accepted |
| Start Date: | 2018-04-29 |
| Finish Date: | 2018-05-01 |
| Conference Place: | Boulder, CO, USA |
| Appears in Collections: | Electrical and Electronic Engineering Faculty of Engineering |