A PYNQ-based Framework for Rapid CNN Prototyping

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Title: A PYNQ-based Framework for Rapid CNN Prototyping
Author(s): 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.
Publication Date: 29-Apr-2018
Date of Acceptance: 6-Mar-2018
URI: http://hdl.handle.net/10044/1/57937
Publisher: IEEE
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)
Copyright Statement: This paper is embargoed until publication.
Publication Status: Accepted
Start Date: 2018-04-29
Finish Date: 2018-05-01
Conference Place: Boulder, CO, USA
Embargo Date: publication subject to indefinite embargo
Appears in Collections:Faculty of Engineering
Electrical and Electronic Engineering



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