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Software Framework for Runtime-Adaptive and Secure Deep Learning on Heterogeneous Architectures

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Software Framework for Runtime-Adaptive and Secure Deep Learning on Heterogeneous Architectures Ref. Ares(2019)7000554 - 12/11/2019 software framework for runtime-Adaptive and secure deep Learning On Heterogeneous Architectures Project Number 780788 Project Acronym ALOHA D6.2 Exploitation plan – First Update Work WP6 Lead Beneficiary: ST-I Package: Type: Report Dissemination level: Public Due Date: 12/11/2019 Delivery: 12/11/2019 Version: v1.1 Brief description: This document presents the exploitation plan of the ALOHA project, including the individual exploitation strategies from each partner of the Consortium. The document includes an analysis of the different markets that could benefit from the technology developed during the project, the identified project exploitable results, the timeline of the already planned activities. IPR management and related standards have also been considered. This document is the first update of deliverable D6.1, submitted at month M4. This is a project living document, which is going to be updated by partners during the project lifetime. ALOHA – 780788 D6.2 Exploitation plan – First Update Deliverable Author(s): Name Beneficiary Giulio URLINI ST-I Daniela Loi UNICA Deliverable Revision History: Reviewer Beneficiary Issue Date Version Comments Giulio URLINI (ST-I) 30/05/2019 0.0 First version of the ToC Francesco PAPARIELLO (ST-I) 07/06/2019 0.1 First update Daniela Loi (UNICA) 12/06/2019 0.2 Review and integration of UNICA plan Francesca Palumbo (UNISS) 13/06/2019 0.3 Review and integration of UNISS plan Francesco Papariello (ST-I) 14/06/2019 0.4 Review and integration of some contributions Francesco Conti (ETHZ) 17/06/2019 0.5 Integration of ETHZ contribution Adriano Souza Ribeiro (PKE) 17/06/2019 0.6 Integration of PKE contribution Cristina Chesta (REPLY) 18/06/2019 0.7 Integration of REPLY contribution Battista Biggio (PLURIBUS) 18/06/2019 0.8 Integration of PLURIBUS contribution Francesco Papariello (ST-I) 19/06/2019 0.9 Integration of UvA, IBM, MaxQ contributions Daniela Loi (UNICA) 21/06/2019 0.12 Integration of all parts Daniela Loi (UNICA) 25/06/2019 0.15 Integration of all contribution on section 6 David Solans (UPF) 25/06/2019 0.16 Integration of UPF contribution Daniela Loi (UNICA) 27/06/2019 1.0 Final version Added section 9 as an answer to reviewers’ requests after the M18 review, clarifying: • Detail on the exploitation plans of industrial partners Paolo Meloni (UNICA) 11/11/2019 1.1 • Detail on already planned user-engagement activities • Lean canvas and detail on management of the lean startup approach 2 Copyright - This document has been produced and funded under the ALOHA H2020 Grant Agreement 780788. Unless officially marked PUBLIC, this document and its contents remain the property of the beneficiaries of the ALOHA Consortium and may not be distributed or reproduced without the express written approval of the project Consortium. ALOHA – 780788 D6.2 Exploitation plan – First Update Disclaimer This document may contain material that is copyright of certain ALOHA beneficiaries, and may not be reproduced, copied, or modified in whole or in part for any purpose without written permission from the ALOHA Consortium. The commercial use of any information contained in this document may require a license from the proprietor of that information. The information in this document is provided “as is” and no guarantee or warranty is given that the information is fit for any particular purpose. The user thereof uses the information at its sole risk and liability. The ALOHA Consortium is the following: # Participant Legal Name Acronym Country 1 STMICROELECTRONICS SRL ST-I Italy 2 UNIVERSITA’ DEGLI STUDI DI CAGLIARI UNICA Italy 3 UNIVERSITEIT VAN AMSTERDAM UVA Netherlands 4 UNIVERSITEIT LEIDEN UL Netherlands 5 EIDGENOESSISCHE TECHNISCHE HOCHSCHULE ZUERICH ETHZ Switzerland 6 UNIVERSITA’ DEGLI STUDI DI SASSARI UNISS Italy 7 PKE HOLDING AG PKE Austria 81 CA TECHNOLOGIES DEVELOPMENT SPAIN SA CA Spain 9 SOFTWARE COMPETENCE CENTER HAGENBERG GMBH SCCH Austria 10 SANTER REPLY SPA REPLY Italy 11 IBM ISRAEL - SCIENCE AND TECHNOLOGY LTD IBM Israel 12 SYSTHMATA YPOLOGISTIKIS ORASHS IRIDA LABS AE IL Greece 13 PLURIBUS ONE SRL PLURIBUS Italy 14 MAXQ ARTIFICIAL INTELLIGENCE, LTD (formerly MaxQ-AI Israel MEDYMATCH TECHNOLOGY, LTD) (formerly MM) 152 UNIVERSIDAD POMPEU FABRA UPF Spain 1 The participation of CA TECHNOLOGIES DEVELOPMENT SPAIN SA (CA) has been terminated on May 9th, 2019. 2 UNIVERSIDAD POMPEU FABRA (UPF) has been added as new beneficiary on May 29th, 2019. 3 Copyright - This document has been produced and funded under the ALOHA H2020 Grant Agreement 780788. Unless officially marked PUBLIC, this document and its contents remain the property of the beneficiaries of the ALOHA Consortium and may not be distributed or reproduced without the express written approval of the project Consortium. ALOHA – 780788 D6.2 Exploitation plan – First Update Table of Contents 1 Executive Summary .................................................................................................................. 7 1.1 Advances in comparison to previous version .................................................................................. 7 1.2 Relationships with other deliverables ............................................................................................ 7 1.3 Acronyms and abbreviations ......................................................................................................... 8 2 The ALOHA business context .................................................................................................... 9 2.1 Stakeholders ............................................................................................................................... 10 3 Market analysis ..................................................................................................................... 11 3.1 Customers ................................................................................................................................... 12 3.1.1 Speech recognition ............................................................................................................................................ 12 3.1.2 Surveillance ....................................................................................................................................................... 14 3.1.3 Medical devices ................................................................................................................................................. 16 3.2 Engineers .................................................................................................................................... 16 3.3 DL Architecture ........................................................................................................................... 19 3.4 DL Frameworks ........................................................................................................................... 20 3.5 Infrastructure .............................................................................................................................. 21 3.6 Devices ....................................................................................................................................... 22 4 ALOHA Key Results ................................................................................................................. 24 4.1 KR1: ALOHA tool flow .................................................................................................................. 24 4.2 KR2: DNN approximation tool for parsimonious inference ............................................................ 25 4.3 KR3: Approximated DNNs for industry-standard benchmark problems ......................................... 26 4.4 KR4: CNN-to-DataFlow model conversion tool ............................................................................. 26 4.5 KR5a: DSE Engine ........................................................................................................................ 27 4.6 KR5b: Training Engine .................................................................................................................. 28 4.7 KR6: Security evaluation of deep neural networks and mitigation strategies ................................. 28 4.8 KR7: Dataset bias detection ......................................................................................................... 30 4.9 KR8: Post-Training tool for Parsimonious Inference ...................................................................... 31 4.10 KR9: Sesame tool for application partitioning & mapping exploration .......................................... 32 4.11 KR10: Architecture Optimization Workbench ............................................................................... 33 4.12 KR11: Automated adaptive middleware generation/customization .............................................. 35 4.13 KR12: Other key exploitation items: Reference platforms ............................................................. 36 5 Exploitation strategy .............................................................................................................. 37 5.1 Exploitation of the ALOHA tools through the use case providers ................................................... 37 5.1.1 PKE..................................................................................................................................................................... 37 4 Copyright - This document has been produced and funded under the ALOHA H2020 Grant Agreement 780788.
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