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software framework for runtime-Adaptive and secure deep Learning On Heterogeneous Architectures

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Deep Learning (DL) algorithms are an extremely promising instrument in artificial intelligence, achieving very high performance in numerous recognition, identification, and classification tasks. To foster their pervasive adoption in a vast scope of new applications and markets, a step forward is needed towards the implementation of the on-line classification task (called inference) on low-power embedded systems, enabling a shift to the edge computing paradigm. Nevertheless, when DL is moved at the edge, severe performance requirements must coexist with tight constraints in terms of power/energy consumption, posing the need for parallel and energy-efficient heterogeneous computing platforms. Unfortunately, programming for this kind of architectures requires advanced skills and significant effort, also considering that DL algorithms are designed to improve precision, without considering the limitations of the device that will execute the inference. Thus, the deployment of DL algorithms on heterogeneous architectures is often unaffordable for SMEs and midcaps without adequate support from software development tools.
The main goal of ALOHA is to facilitate implementation of DL on heterogeneous low-energy computing platforms. To this aim, the project will develop a software development tool flow, automating:
• algorithm design and analysis;
• porting of the inference tasks to heterogeneous embedded architectures, with optimized mapping and scheduling;
• implementation of middleware and primitives controlling the target platform, to optimize power and energy savings.
During the development of the ALOHA tool flow, several main features will be addressed, such as architecture-awareness (the features of the embedded architecture will be considered starting from the algorithm design), adaptivity, security, productivity, and extensibility.
ALOHA will be assessed over three different use-cases, involving surveillance, smart industry automation, and medical application domains

Wissenschaftliches Gebiet

Schlüsselbegriffe

Programm/Programme

Thema/Themen

Aufforderung zur Vorschlagseinreichung

H2020-ICT-2016-2017

Andere Projekte für diesen Aufruf anzeigen

Unterauftrag

H2020-ICT-2017-1

Finanzierungsplan

RIA - Research and Innovation action

Koordinator

STMICROELECTRONICS SRL
Netto-EU-Beitrag
€ 879 950,00
Adresse
Via c.olivetti 2
20864 Agrate brianza
Italien

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Region
Nord-Ovest Lombardia Monza e della Brianza
Aktivitätstyp
Private for-profit entities (excluding Higher or Secondary Education Establishments)
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Weitere Finanzmittel
€ 0,00

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Letzte Aktualisierung: 7 September 2023

Mein Booklet

Permalink: https://cordis.europa.eu/project/id/780788/de

European Union, 2023