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

Leistungen

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

Veröffentlichungen

Data in Brief

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

WRIT: Web Request Integrity and Attestation Against Malicious Browser Extensions

Autoren: Giorgos Vasiliadis, Apostolos Karampelas, Alexandros Shevtsov, Panagiotis Papadopoulos, Sotiris Ioannidis, Alexandros Kapravelos
Veröffentlicht in: IEEE Transactions on Dependable and Secure Computing, Ausgabe 21, 2024, ISSN 1545-5971
Herausgeber: 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

Autoren: Caelin Kaplan, Chuan Xu, Othmane Marfoq, Giovanni Neglia, Anderson Santana de Oliveira
Veröffentlicht in: Proceedings on Privacy Enhancing Technologies, Ausgabe 2024, 2023, ISSN 2299-0984
Herausgeber: Privacy Enhancing Technologies Symposium Advisory Board
DOI: 10.56553/popets-2024-0031

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

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

Fast Transciphering Via Batched And Reconfigurable LUT Evaluation

Autoren: Leonard Schild, Aysajan Abidin, Bart Preneel
Veröffentlicht in: IACR Transactions on Cryptographic Hardware and Embedded Systems, Ausgabe 2024, 2024, ISSN 2569-2925
Herausgeber: Universitatsbibliothek der Ruhr-Universitat Bochum
DOI: 10.46586/tches.v2024.i4.205-230

ShareBERT: Embeddings Are Capable of Learning Hidden Layers

Autoren: Jia Cheng Hu, Roberto Cavicchioli, Giulia Berardinelli, Alessandro Capotondi
Veröffentlicht in: Proceedings of the AAAI Conference on Artificial Intelligence, Ausgabe 38, 2024, ISSN 2374-3468
Herausgeber: 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

Autoren: Viviane Potocnik; Alfio Di Mauro; Christoph Leitner; Moritz Scherer; Georg Rutishauser; Lorenzo Lamberti; Luca Benini
Veröffentlicht in: 2024
Herausgeber: www.researchsquare.com
DOI: 10.21203/rs.3.rs-4023416/v1

Leveraging AutoEncoders and chaos theory to improve adversarial example detection

Autoren: Anibal Pedraza; Oscar Deniz; Harbinder Singh; Gloria Bueno
Veröffentlicht in: Neural Computing and Applications, 2024, ISBN 1826518275
Herausgeber: 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

Autoren: Vinamra Sharma, Danilo Pau, José Cano
Veröffentlicht in: 2024 IEEE 8th Forum on Research and Technologies for Society and Industry Innovation (RTSI), 2024
Herausgeber: IEEE
DOI: 10.1109/RTSI61910.2024.10761203

FedStale: leveraging Stale Updates in Federated Learning

Autoren: Angelo Rodio, Giovanni Neglia
Veröffentlicht in: Frontiers in Artificial Intelligence and Applications, ECAI 2024, 2024
Herausgeber: IOS Press
DOI: 10.3233/FAIA240849

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

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

Scheduling Machine Learning Compressible Inference Tasks with Limited Energy Budget

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

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

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

Leistungen

Veröffentlichungen

Diese Seite teilen

Herunterladen