Explore the words cloud of the ALOHA project. It provides you a very rough idea of what is the project "ALOHA" about.
The following table provides information about the project.
Coordinator |
STMICROELECTRONICS SRL
Organization address contact info |
Coordinator Country | Italy [IT] |
Project website | https://www.aloha-h2020.eu/ |
Total cost | 5˙976˙415 € |
EC max contribution | 5˙976˙415 € (100%) |
Programme |
1. H2020-EU.2.1.1. (INDUSTRIAL LEADERSHIP - Leadership in enabling and industrial technologies - Information and Communication Technologies (ICT)) |
Code Call | H2020-ICT-2017-1 |
Funding Scheme | RIA |
Starting year | 2018 |
Duration (year-month-day) | from 2018-01-01 to 2020-12-31 |
Take a look of project's partnership.
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
Report on tool flow integration | Documents, reports | 2020-01-14 15:01:00 |
Report on the exploitation activities | Documents, reports | 2020-01-14 15:01:15 |
Open Data Management Plan – First Update | Open Research Data Pilot | 2020-01-14 15:01:02 |
First release of the automated tool for application partitioning and mapping | Demonstrators, pilots, prototypes | 2020-01-14 15:01:14 |
Second progress and management report | Documents, reports | 2020-01-14 15:00:58 |
First release of the hardware abstraction layer utilities | Demonstrators, pilots, prototypes | 2020-01-14 15:01:15 |
Plan for the dissemination and communication - First Update | Documents, reports | 2020-01-14 15:01:16 |
Report on hardware abstraction layer techniques – Update | Documents, reports | 2020-01-14 15:01:17 |
Report on automated application partitioning and mapping – Update | Documents, reports | 2020-01-14 15:01:13 |
Dissemination and communication report | Documents, reports | 2020-01-14 15:00:58 |
Exploitation plan – First Update | Documents, reports | 2020-01-14 14:24:29 |
Report on automated algorithm configuration – Update | Documents, reports | 2020-01-14 15:02:05 |
First release of the automated algorithm configuration tool | Demonstrators, pilots, prototypes | 2020-01-14 15:01:14 |
Report on hardware abstraction layer techniques | Documents, reports | 2019-10-02 13:38:37 |
Project quality handbook | Documents, reports | 2019-10-02 13:38:37 |
Report on general specifications and interface definition | Documents, reports | 2019-10-02 13:38:37 |
Kick-off progress report | Documents, reports | 2019-10-02 13:38:37 |
Plan for the dissemination and communication | Documents, reports | 2019-10-02 13:38:37 |
Report on automated algorithm configuration | Documents, reports | 2019-10-02 13:38:37 |
First progress and management report | Documents, reports | 2019-10-02 13:38:37 |
Report on automated application partitioning and mapping | Documents, reports | 2019-10-02 13:38:37 |
Project digital presence | Demonstrators, pilots, prototypes | 2019-10-02 13:38:37 |
Open Data Management Plan | Open Research Data Pilot | 2019-10-02 13:38:37 |
Exploitation plan | Documents, reports | 2019-10-02 13:38:37 |
Take a look to the deliverables list in detail: detailed list of ALOHA deliverables.
year | authors and title | journal | last update |
---|---|---|---|
2018 |
Battista Biggio, Fabio Roli Wild patterns: Ten years after the rise of adversarial machine learning published pages: 317-331, ISSN: 0031-3203, DOI: 10.1016/j.patcog.2018.07.023 |
Pattern Recognition 84 | 2019-10-02 |
2019 |
Lin Li, Carlo Sau, Tiziana Fanni, Jingui Li, Timo Viitanen, François Christophe, Francesca Palumbo, Luigi Raffo, Heikki Huttunen, Jarmo Takala, Shuvra S. Bhattacharyya An integrated hardware/software design methodology for signal processing systems published pages: 1-19, ISSN: 1383-7621, DOI: 10.1016/j.sysarc.2018.12.010 |
Journal of Systems Architecture 93 | 2019-10-02 |
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The information about "ALOHA" are provided by the European Opendata Portal: CORDIS opendata.