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Adaptive and secure deep Learning On Heterogeneous Architectures

DL algorithms are designed to improve precision, without considering the limitations of the device that will execute them. Thus, the deployment of DL algorithms on heterogeneous architectures is highly time consuming without adequate support from software development tools. ​

Moreover, DL applications present bias in their decisions resulting in ethical problems in many companies, big and small. ​

The objective of ALOHA is to develop a tool flow for helping developers to design, build and deploy DL application on (embedded) heterogeneous architectures. This tool flow will also include security tools to improve resilience of the systems to attacks and secure data in an edge computing paradigm. ​

From CA’s perspective, this project will help us answer the question of how we can use Agile and DevSecOps to build DL applications while minimizing data bias and supporting fairness and ethical use.

Project Partners

ST Microelectronics

Università degli Studi di Cagliari

Universiteit van Amsterdam

Universiteit Leiden

Eidgenössische Technische Hochschule Zürich

Università degli Studi di Sassari

PKE Electronics AG

Software Competence Center Hagenberg GmbH

Santer Reply SpA

IBM Israel Science & Technology Ltd


Pluribus One srl

MedyMatch Technology Ltd