A network of excellence for distributed, trustworthy, efficient and scalable AI at the Edge

CORDIS fournit des liens vers les livrables publics et les publications des projets HORIZON.

Les liens vers les livrables et les publications des projets du 7e PC, ainsi que les liens vers certains types de résultats spécifiques tels que les jeux de données et les logiciels, sont récupérés dynamiquement sur OpenAIRE .

Livrables

Final data management plan

Plan for management of data in the NoE and in the consortium - final version

Initial data management plan

Plan for management of data in the NoE and in the consortium - intial version

Seminars, workshop and events - Event Nr 1

Dissemination events, multiplier events

Roundtable on Edge AI functional and non-functional requirements

Expert workshop for requirements gathering and discussion

Workshops (co-organized with other pillars) - Workshop Nr 1

Two-day workshop for alignment with all pillars of the EU AI Lighthouse

Intermediate report and risk log - Report Nr 1

Updated report and log for risk prevention and mitigation (every 6 months)

dAIEDGE Communication, Dissemination and Exploitation plan

Plan of the project communication, dissemination and exploitation

Report on Edge AI functional and non-functional requirements

Comprehensive report on edge AI requirements

Call Announcement and Guide for Applicants - Call Nr 1

Open Call Package documents

Evolutive infrastructure inventory - Report Nr 1

Inventory of existing infrastructure for edge AU

Quality assurance plan

Measures for ensuring quality

Risk management plan

Plan and measures for risk prevention and mitigation

Distributed network design

Design and architecture of the distributed network of frameworks

Intermediate reports and risk log - Report Nr 2

Updated report and log for risk prevention and mitigation (every 6 months)

Reference document on European AI Lighthouse implementation

Plans and recommendations organisation of the EU AI Lighthouse

Evolutive infrastructure inventory - Report Nr 2

Inventory of existing infrastructure for edge AU

Report on Use Case definition, demonstration setup and validation plan

Complete and refined definition of the three use cases with evaluation plan

Publications

Data in Brief

Auteurs: Jesus Ruiz-Santaquiteria; Juan D. Muñoz; Francisco J. Maigler; Oscar Deniz; Gloria Bueno
Publié dans: Data in Brief, 2023, ISSN 2352-3409
Éditeur: Elsevier BV
DOI: 10.1016/j.dib.2024.110030

WRIT: Web Request Integrity and Attestation Against Malicious Browser Extensions

Auteurs: Giorgos Vasiliadis, Apostolos Karampelas, Alexandros Shevtsov, Panagiotis Papadopoulos, Sotiris Ioannidis, Alexandros Kapravelos
Publié dans: IEEE Transactions on Dependable and Secure Computing, Numéro 21, 2024, ISSN 1545-5971
Éditeur: Institute of Electrical and Electronics Engineers (IEEE)
DOI: 10.1109/TDSC.2023.3322516

A Cautionary Tale: On the Role of Reference Data in Empirical Privacy Defenses

Auteurs: Caelin Kaplan, Chuan Xu, Othmane Marfoq, Giovanni Neglia, Anderson Santana de Oliveira
Publié dans: Proceedings on Privacy Enhancing Technologies, Numéro 2024, 2023, ISSN 2299-0984
Éditeur: Privacy Enhancing Technologies Symposium Advisory Board
DOI: 10.56553/popets-2024-0031

DLAS: A Conceptual Model for Across-Stack Deep Learning Acceleration

Auteurs: Perry Gibson, Jose Cano, Elliot Crowley, Amos Storkey, Michael O'Boyle
Publié dans: ACM Transactions on Architecture and Code Optimization, 2024, ISSN 1544-3566
Éditeur: Association for Computing Machinery (ACM)
DOI: 10.1145/3688609

Fast Transciphering Via Batched And Reconfigurable LUT Evaluation

Auteurs: Leonard Schild, Aysajan Abidin, Bart Preneel
Publié dans: IACR Transactions on Cryptographic Hardware and Embedded Systems, Numéro 2024, 2024, ISSN 2569-2925
Éditeur: Universitatsbibliothek der Ruhr-Universitat Bochum
DOI: 10.46586/tches.v2024.i4.205-230

ShareBERT: Embeddings Are Capable of Learning Hidden Layers

Auteurs: Jia Cheng Hu, Roberto Cavicchioli, Giulia Berardinelli, Alessandro Capotondi
Publié dans: Proceedings of the AAAI Conference on Artificial Intelligence, Numéro 38, 2024, ISSN 2374-3468
Éditeur: Association for the Advancement of Artificial Intelligence (AAAI)
DOI: 10.1609/aaai.v38i16.29781

Kraken: An Open-Source RISC-V SoC for Ultra-Low Power Multi-Modal Perception

Auteurs: Viviane Potocnik; Alfio Di Mauro; Christoph Leitner; Moritz Scherer; Georg Rutishauser; Lorenzo Lamberti; Luca Benini
Publié dans: 2024
Éditeur: www.researchsquare.com
DOI: 10.21203/rs.3.rs-4023416/v1

Leveraging AutoEncoders and chaos theory to improve adversarial example detection

Auteurs: Anibal Pedraza; Oscar Deniz; Harbinder Singh; Gloria Bueno
Publié dans: Neural Computing and Applications, 2024, ISBN 1826518275
Éditeur: Neural Computing and Applications (2024)
DOI: 10.1007/s00521-024-10141-1

Efficient Tiny Machine Learning for Human Activity Recognition on Low-Power Edge Devices

Auteurs: Vinamra Sharma, Danilo Pau, José Cano
Publié dans: 2024 IEEE 8th Forum on Research and Technologies for Society and Industry Innovation (RTSI), 2024
Éditeur: IEEE
DOI: 10.1109/RTSI61910.2024.10761203

FedStale: leveraging Stale Updates in Federated Learning

Auteurs: Angelo Rodio, Giovanni Neglia
Publié dans: Frontiers in Artificial Intelligence and Applications, ECAI 2024, 2024
Éditeur: IOS Press
DOI: 10.3233/FAIA240849

Scheduling with Fully Compressible Tasks: Application to Deep Learning Inference with Neural Network Compression

Auteurs: Tiago Da Silva Barros, Frédéric Giroire, Ramon Aparicio-Pardo, Stéphane Pérennes, Emanuele Natale
Publié dans: 2024 IEEE 24th International Symposium on Cluster, Cloud and Internet Computing (CCGrid), 2024
Éditeur: IEEE
DOI: 10.1109/CCGrid59990.2024.00045

Scheduling Machine Learning Compressible Inference Tasks with Limited Energy Budget

Auteurs: Tiago Da Silva Barros, Davide Ferre, Frederic Giroire, Ramon Aparicio-Pardo, Stephane Perennes
Publié dans: Proceedings of the 53rd International Conference on Parallel Processing, 2024
Éditeur: ACM
DOI: 10.1145/3673038.3673106

FedLoRa: IoT Spectrum Sensing Through Fast and Energy-Efficient Federated Learning in LoRa Networks

Auteurs: Fabio Busacca, Stefano Mangione, Giovanni Neglia, Ilenia Tinnirello, Sergio Palazzo, Francesco Restuccia
Publié dans: 2024 IEEE 21st International Conference on Mobile Ad-Hoc and Smart Systems (MASS), 2024
Éditeur: IEEE
DOI: 10.1109/MASS62177.2024.00047

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