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Deep-Learning and HPC to Boost Biomedical Applications for Health

Deliverables

The Runtime system for DeepHealth libraries

This deliverable will release the HPC run-time with all functionalities and a report describing it. Intermediate delivery at M15.

ECVL Hw algorithms and adaptation to HPC infr.

Report describing the adaptation of heterogeneous components to the algorithms implemented on the library. Deliverable associated to task T3.2. Draft in M17.

Training toolkit

Will include all the specifications and requirements needed for Deep Learning algorithms training: frameworks, neural networks architecture & dataset (T1.4).

ECVL adaptation to cloud environments

Includes activities of Task 2.4.

EDDLL adaptation to Cloud

Includes activities of Task 2.4.

Dissemination and comm. plans and report

This deliverable will provide an elaborate analysis of the stakeholder ecosystem and a plan of the targeted dissemination activities with a continuous reporting style. Tasks T7.1 and T7.2 involved.

Infrastructure & application adaptation requirements

Will include full detail of HPC infrastructure (T1.3) and optimizations for heterogeneous components and cloud (T1.8).

EDDLL Hardw. algorithms and adaptation to HPC

Report describing the adaptation of heterogeneous components to the algorithms implemented on the library. Deliverable associated to task T2.3. A draft in M17.

API specifications for EDDLL and ECVL libraries

Will describe in full detail the API for the libraries to deploy, the deep-learning one (T1.5) and the computer vision one (T1.6).

Dissemination and comm. plans and report (II)

This deliverable will provide an elaborate analysis of the stakeholder ecosystem and a plan of the targeted dissemination activities with a continuous reporting style. Tasks T7.1 and T7.2 involved.

Efficient HPC infrastr. for DeepHealth libraries

This deliverable will report T5.1 and the advances of T5.2 and T5.3. Intermediate report at M15.

ECVL Toolkit front-end

Toolkit manual associated to Task T3.4.

EDDLL Toolkit front-end

Toolkit manual associated to Task T2.5.

ECVL library

Documentation describing the deployed library from Tasks T3.1, T3.5. Draft in M17.

EDDLL library

Documentation describing deployed library from Tasks T2.1, T2.2 and T2.6. A draft in M17.

ORDP: Open Research Data Pilot

This deliverable deals with the data collected and generated during the project, and central data, publications, etc.

Searching for OpenAIRE data...

Publications

Deep-Learning and HPC to Boost Biomedical Applications for Health (DeepHealth)

Author(s): Monica Caballero, Jon Ander Gomez, Aimilia Bantouna
Published in: 2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS), 2019, Page(s) 150-155
DOI: 10.1109/CBMS.2019.00040

An Event-Based System for Low-Power ECG QRS Complex Detection

Author(s): Silvio Zanoli, Tomas Teijeiro, Fabio Montagna, David Atienza
Published in: 2020 Design, Automation & Test in Europe Conference & Exhibition (DATE), 2020, Page(s) 258-263
DOI: 10.23919/DATE48585.2020.9116498

Noise-Resilient and Interpretable Epileptic Seizure Detection

Author(s): Hitchcock Thomas, Anthony; Aminifar, Amir; Atienza, David
Published in: 2020 IEEE International Symposium on Circuits and Systems (ISCAS)., Issue 6, 2020
DOI: 10.5281/zenodo.3903314

Resource-Aware Distributed Epilepsy Monitoring Using Self-Awareness From Edge to Cloud

Author(s): Farnaz Forooghifar, Amir Aminifar, David Atienza
Published in: IEEE Transactions on Biomedical Circuits and Systems, Issue 13/6, 2019, Page(s) 1338-1350, ISSN 1932-4545
DOI: 10.1109/tbcas.2019.2951222

MAGNETIC: Multi-Agent Machine Learning-Based Approach for Energy Efficient Dynamic Consolidation in Data Centers

Author(s): Kawsar Haghshenas, Ali Pahlevan, Marina Zapater, Siamak Mohammadi, David Atienza
Published in: IEEE Transactions on Services Computing, 2019, Page(s) 1-1, ISSN 1939-1374
DOI: 10.1109/tsc.2019.2919555