SAMO: optimised mapping of convolutional neural networks to streaming architectures
File(s)2112.00170v2.pdf (800.25 KB)
Accepted version
Author(s)
Montgomerie-Corcoran, A
Yu, Z
Bouganis, C-S
Type
Conference Paper
Abstract
Significant effort has been placed on the development of toolflows that map Convolutional Neural Network (CNN) models to Field Programmable Gate Arrays (FPGAs) with the aim of automating the production of high performance designs for a diverse set of applications. However, within these toolflows, the problem of finding an optimal mapping is often overlooked, with the expectation that the end user will tune their generated hardware for their desired platform. This is particularly prominent within Streaming Architecture toolflows, where there is a large design space to be explored. In this work, we establish the framework SAMO: a Streaming Architecture Mapping Optimiser. SAMO exploits the structure of CNN models and the common features that exist in Streaming Architectures, and casts the mapping optimisation problem under a unified methodology. Furthermore, SAMO explicitly explores the re-configurability property of FPGAs, allowing the methodology to overcome mapping limitations imposed by certain toolflows under resource-constrained scenarios, as well as improve on the achievable throughput. Three optimisation methods - Brute-Force, Simulated Annealing and Rule-Based - have been developed in order to generate valid, high performance designs for a range of target platforms and CNN models. Results show that SAMO-optimised designs can achieve 4x-20x better performance compared to existing hand-tuned designs. The SAMO framework is open-source: https://github.com/AlexMontgomerie/samo.
Date Issued
2022-02-13
Online Publication Date
2024-03-05T10:05:17Z
Date Acceptance
2022-08-29
ISBN
978-1-6654-7390-3
ISSN
1946-1488
Publisher
IEEE
Start Page
418
End Page
424
Journal / Book Title
2022 32nd International Conference on Field-Programmable Logic and Applications (FPL)
Copyright Statement
Copyright © 2023 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.
Identifier
https://ieeexplore.ieee.org/document/10035202
https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000975890500057&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=a2bf6146997ec60c407a63945d4e92bb
Source
32nd International Conference on Field-Programmable Logic and Applications (FPL)
Publication Status
Published
Start Date
2022-08-29
Finish Date
2022-09-02
Country
Belfast, United Kingdom
Date Publish Online
2023-02-13