The University of Southampton
University of Southampton Institutional Repository

Dataset supporting the conference paper "Fluid dynamic DNNs for reliable and adaptive distributed inference on edge devices"

Dataset supporting the conference paper "Fluid dynamic DNNs for reliable and adaptive distributed inference on edge devices"
Dataset supporting the conference paper "Fluid dynamic DNNs for reliable and adaptive distributed inference on edge devices"
This dataset supports the publication: "Fluid Dynamic DNNs for Reliable and Adaptive Distributed Inference on Edge Devices" by Lei Xun, Mingyu Hu, Hengrui Zhao, Amit Kumar Singh, Jonathon Hare, Geoff V. Merrett CONFERENCE: Design, Automation and Test in Europe Conference 2024 This dataset includes the experimental results for Figure 2 of the paper, showing the throughput and accuracy of the different models (static, dynamic and fluid) considered under different distributed-system cases (master & worker, master, worker). This dataset contains: -'data.csv': Data supporting Fig. 2. The throughput and accuracy of the different models (static, dynamic and fluid) considered under different distributed-system cases (master & worker, master, worker). Related projects: International Centre for Spatial Computational Learning
University of Southampton
Hu, Mingyu
686551f3-f76b-471d-b424-71a5c68851da
Xun, Lei
d30d0c37-7c17-4eed-b02c-1a0f81844f17
Zhao, Hengrui
9a7e2ba5-4932-4188-8aef-ae39d17fca46
Singh, Amit Kumar
bded7886-24ab-4a24-8539-f8fe106426ac
Hare, Jonathon
65ba2cda-eaaf-4767-a325-cd845504e5a9
Merrett, Geoff
89b3a696-41de-44c3-89aa-b0aa29f54020
Hu, Mingyu
686551f3-f76b-471d-b424-71a5c68851da
Xun, Lei
d30d0c37-7c17-4eed-b02c-1a0f81844f17
Zhao, Hengrui
9a7e2ba5-4932-4188-8aef-ae39d17fca46
Singh, Amit Kumar
bded7886-24ab-4a24-8539-f8fe106426ac
Hare, Jonathon
65ba2cda-eaaf-4767-a325-cd845504e5a9
Merrett, Geoff
89b3a696-41de-44c3-89aa-b0aa29f54020

Hu, Mingyu (2023) Dataset supporting the conference paper "Fluid dynamic DNNs for reliable and adaptive distributed inference on edge devices". University of Southampton doi:10.5258/SOTON/D2886 [Dataset]

Record type: Dataset

Abstract

This dataset supports the publication: "Fluid Dynamic DNNs for Reliable and Adaptive Distributed Inference on Edge Devices" by Lei Xun, Mingyu Hu, Hengrui Zhao, Amit Kumar Singh, Jonathon Hare, Geoff V. Merrett CONFERENCE: Design, Automation and Test in Europe Conference 2024 This dataset includes the experimental results for Figure 2 of the paper, showing the throughput and accuracy of the different models (static, dynamic and fluid) considered under different distributed-system cases (master & worker, master, worker). This dataset contains: -'data.csv': Data supporting Fig. 2. The throughput and accuracy of the different models (static, dynamic and fluid) considered under different distributed-system cases (master & worker, master, worker). Related projects: International Centre for Spatial Computational Learning

Text
data.csv - Dataset
Available under License Creative Commons Attribution.
Download (532B)
Text
README.txt - Text
Available under License Creative Commons Attribution.
Download (1kB)

More information

Published date: 29 November 2023

Identifiers

Local EPrints ID: 486246
URI: http://eprints.soton.ac.uk/id/eprint/486246
PURE UUID: 0776c3aa-7006-48f3-afb3-f336e3aea93f
ORCID for Jonathon Hare: ORCID iD orcid.org/0000-0003-2921-4283
ORCID for Geoff Merrett: ORCID iD orcid.org/0000-0003-4980-3894

Catalogue record

Date deposited: 15 Jan 2024 17:56
Last modified: 28 Mar 2024 05:01

Export record

Altmetrics

Contributors

Creator: Mingyu Hu
Contributor: Lei Xun
Contributor: Hengrui Zhao
Research team head: Amit Kumar Singh
Research team head: Jonathon Hare ORCID iD
Research team head: Geoff Merrett ORCID iD

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×