Distributed Inference for Energy-efficient Monitoring at the Network Edge

Información del proyecto

DIME

Identificador del acuerdo de subvención: 101062011

DOI

10.3030/101062011

Proyecto cerrado

Fecha de la firma de la CE 19 Mayo 2022

Fecha de inicio 1 Junio 2022

Fecha de finalización 31 Mayo 2024

Financiado con arreglo a

• Marie Skłodowska-Curie Actions (MSCA)

Coste total

Sin datos

Aportación de la UE

€ 181 152,96

Coordinado por

FUNDACION IMDEA NETWORKS
Spain

CORDIS proporciona enlaces a los documentos públicos y las publicaciones de los proyectos de los programas marco HORIZONTE.

Los enlaces a los documentos y las publicaciones de los proyectos del Séptimo Programa Marco, así como los enlaces a algunos tipos de resultados específicos, como conjuntos de datos y «software», se obtienen dinámicamente de OpenAIRE .

Resultado final

Data Management Plan (1)

Data Management Plan

The Data Management Plan describes the data management life cycle for all data sets that will be collected processed or generated by the action It is a document describing what data will be collected processed or generated and following what methodology and standards whether and how this data will be shared andor made open and how it will be curated and preserved

Documents, reports (1)

Communication, Dissemination & Outreach Plan

The plan describes the planned measures to maximize the impact of the project, including the dissemination and exploitation measures that are planned, and the target group(s) addressed. Regarding communication measures and public engagement strategy, the aim is to inform and reach out to society and show the activities performed, and the use and the benefits the project will have for citizens.

Publicaciones

Conference proceedings (3)

Improved Decision Module Selection for Hierarchical Inference in Resource-Constrained Edge Devices

Autores: Adarsh Prasad Behera, Roberto Morabito, Joerg Widmer, and Jaya Prakash Champati
Publicado en: ISBN 9781450399906
Editor: ACM
DOI: 10.1145/3570361.3615732

An Offloading Algorithm for Maximizing Inference Accuracy on Edge Device in an Edge Intelligence System

Autores: Andrea Fresa and Jaya Prakash Champati
Publicado en: MSWIM '22, Edición 25TH, 2022, ISBN 9781450394826
Editor: Association for Computing Machinery (ACM)
DOI: 10.1145/3551659.3559044

The Case for Hierarchical Deep Learning Inference at the Network Edge

Autores: Ghina Al-Atat, Andrea Fresa, Adarsh Prasad Behera, Vishnu Narayanan Moothedath, James Gross, and Jaya Prakash Champati
Publicado en: ISBN 9798400702129
Editor: ACM
DOI: 10.1145/3597062.3597278

Peer reviewed articles (2)

Getting the Best Out of Both Worlds: Algorithms for Hierarchical Inference at the Edge

Autores: Vishnu N. Moothedath, Jaya Prakash Champati, James Gross
Publicado en: IEEE Transactions on Machine Learning in Communications and Networking, ISSN 2831-316X
Editor: IEEE
DOI: 10.1109/TMLCN.2024.3366501

Offloading Algorithms for Maximizing Inference Accuracy on Edge Device in an Edge Intelligence System

Autores: A. Fresa and J. P. Champati
Publicado en: IEEE Transactions on Parallel and Distributed Systems, ISSN 1045-9219
Editor: IEEE
DOI: 10.1109/TPDS.2023.3267458