Aloha project

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

Horizon 2020

This project is funded by the European Union’s Horizon 2020 programme, grant agreement No. 780788

Consortium

We are a team of 14 partners from 7 countries

Deep Learning (DL) algorithms are an extremely promising instrument in artificial intelligence. To foster their adoption in new applications and markets, a step forward is needed towards the implementation of DL inference on low-power embedded systems, enabling a shift to the edge computing paradigm. The main goal of ALOHA is to facilitate implementation of DL algorithms on heterogeneous low-energy computing platforms providing automation for optimal algorithm selection, resource allocation and deployment.

Architecture-awareness

The features of the architecture that will execute the inference are taken into account during the whole development process, starting from the early stages such as pre-training hyperparameter optimization and algorithm configuration.

Productivity

The tool flow implements support for agile development methodologies, to be easily adopted by SMEs and midcaps.

Adaptivity

The development process considers that the system should adapt to different operating modes at runtime.



Extensibility

The development process is conceived to support novel processing platforms to be exploitable beyond the end of the project.

Security

The development process automates the introduction of algorithm features and programming techniques improving the resilience of the system to attacks.


Parsimonious inference

In DL, good precision levels can be also obtained using algorithms with reduced complexity. All the optimization utilities in the ALOHA tool flow will consider the effects of algorithm simplification on precision, execution time, energy and power.

Latest News & Events

ALOHA at VISION 2018 (Stuttgart - November 6-8)

VISION is the world’s leading trade fair for machine vision. The who’s who in the machine vision industry meets in Stuttgart and presents an incomparable range of products and services: from sensors to processors, from cables to cameras, from software to illumination systems. However, complete machine vision systems and very specific applications for many different industries are exhibited – from mechanical engineering and the automotive industry through to the medical technology industry and many, many more industries besides.

During VISION 2018.(Messe Stuttgart, Germany, November 6-8), IridaLabs presented the ALOHA architecture and the project's first research findings.

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ALOHA with "WILD PATTERNS" at EUSIPCO2018 (Rome), ECCV2018 (Munich), ACM CCS (Toronto)

Wild Patterns is a Tutorial on Adversarial Machine Learning, periodically held by Pluribus One during several top conferences and events on machine learning and computer vision. The secure machine-learning algorithms and the related security assessment procedures defined within the ALOHA project have been presented by Battista Biggio and Fabio Roli during the last three editions of the tutorial, within EUSIPCO 2018, 26th European Signal Processing Conference (Rome, Italy, September 3-7), then during ECCV 2018, European Conference on Computer Vision.(Munich, Germany, September 8-14), and during ACM CCS 2018, 25th ACM Conference on Computer and Communications Security (Toronto, Canada, October 15-19). Slides of the editions of the Tutorial are available in this article.

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ALOHA in the press (February - June 2018)

In our previous blog entry "ALOHA in the press (January 2018)" we collected a list of articles and blog posts related to the ALOHA's kick off meeting.
In this new post we share our media coverage during the last months of project (February-June).
This list includes articles in German, Italian, English, French, Chinese.

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Contacts

Project Coordinator
Giuseppe Desoli - STMicroelectronics
giuseppe(dot)desoli(at)st(dot)com

Scientific Coordinator
Paolo Meloni - University of Cagliari, EOLAB
paolo(dot)meloni(at)diee(dot)unica(dot)it

Dissemination Manager
Francesca Palumbo - University of Sassari, PolComing
fpalumbo(at)uniss(dot)it

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