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Bonseyes SIGNED

Platform for Open Development of Systems of Artificial Intelligence

Total Cost €

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EC-Contrib. €

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Partnership

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Project "Bonseyes" data sheet

The following table provides information about the project.

Coordinator
NVISO SA 

Organization address
address: PARC SCIENTIFIQUE EPFL
city: LAUSANNE
postcode: 1015
website: n.a.

contact info
title: n.a.
name: n.a.
surname: n.a.
function: n.a.
email: n.a.
telephone: n.a.
fax: n.a.

 Coordinator Country Switzerland [CH]
 Project website https://www.bonseyes.com
 Total cost 8˙593˙952 €
 EC max contribution 5˙018˙025 € (58%)
 Programme 1. H2020-EU.2.1.1. (INDUSTRIAL LEADERSHIP - Leadership in enabling and industrial technologies - Information and Communication Technologies (ICT))
 Code Call H2020-ICT-2016-1
 Funding Scheme RIA
 Starting year 2016
 Duration (year-month-day) from 2016-12-01   to  2020-01-31

 Partnership

Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
1    NVISO SA CH (LAUSANNE) coordinator 0.00
2    ISTRAZIVACKO-RAZVOJNI INSTITUT RT-RK DOO ZA SISTEME ZASNOVANE NA RACUNARIMA NOVI SAD RS (NOVI SAD) participant 802˙500.00
3    ARM LIMITED UK (CAMBRIDGE) participant 717˙418.00
4    THE UNIVERSITY OF EDINBURGH UK (EDINBURGH) participant 630˙924.00
5    THE PROVOST, FELLOWS, FOUNDATION SCHOLARS & THE OTHER MEMBERS OF BOARD OF THE COLLEGE OF THE HOLY & UNDIVIDED TRINITY OF QUEEN ELIZABETH NEAR DUBLIN IE (DUBLIN) participant 582˙628.00
6    INSTITUTE OF COMMUNICATION AND COMPUTER SYSTEMS EL (ATHINA) participant 375˙977.00
7    BLEKINGE TEKNISKA HOGSKOLA SE (KARLSKRONA) participant 344˙127.00
8    ZF FRIEDRICHSHAFEN AG DE (FRIEDRICHSHAFEN) participant 304˙725.00
9    DARWIN DIGITAL DOO BEOGRAD-STARI GRAD RS (BEOGRAD) participant 298˙045.00
10    UNIVERSIDAD DE CASTILLA - LA MANCHA ES (CIUDAD REAL) participant 271˙389.00
11    KLINIKUM RECHTS DER ISAR DER TECHNISCHEN UNIVERSITAT MUNCHEN DE (MUENCHEN) participant 265˙283.00
12    SYNYO GmbH AT (WIEN) participant 260˙808.00
13    BONSEYES COMMUNITY ASSOCIATION CH (LAUSANNE) participant 164˙200.00
14    FACHHOCHSCHULE NORDWESTSCHWEIZ CH (WINDISCH) participant 0.00
15    HAUTE ECOLE SPECIALISEE DE SUISSE OCCIDENTALE CH (DELEMONT) participant 0.00
16    SCIPROM SARL CH (SAINT SULPICE) participant 0.00

Map

 Project objective

The Bonseyes project aims to develop a platform consisting of a Data Marketplace, Deep Learning Toolbox, and Developer Reference Platforms for organizations wanting to adopt Artificial Intelligence in low power IoT devices (“edge computing”), embedded computing systems, or data center servers (“cloud computing”). It will bring about orders of magnitude improvements in efficiency, performance, reliability, security, and productivity in the design and programming of Systems of Artificial Intelligence that incorporate Smart Cyber Physical Systems while solving a chicken-egg problem for organizations who lack access to Data and Models. It’s open software architecture will facilitate adoption of the whole concept on a wider scale.

It aims to address one of the most significant trends in the Internet of Things which is the shifting balance between edge computing and cloud computing. The early days of the IoT have been characterized by the critical role of cloud platforms as application enablers. Intelligent systems have largely relied on the cloud level for their intelligence, and the actual devices of which they consist have been relatively unsophisticated. This old premise is currently being shaken up, as the computing capabilities on the edge level advance faster than those of the cloud level. This paradigm shift—from the connected device paradigm to the intelligent device paradigm opens up numerous opportunities.

To evaluate the effectiveness, technical feasibility, and to quantify the real-world improvements in efficiency, security, performance, effort and cost of adding AI to products and services using the Bonseyes platform, four complementary demonstrators will be built: Automotive Intelligent Safety, Automotive Cognitive Computing, Consumer Emotional Virtual Agent, and Healthcare Patient Monitoring. Bonseyes platform capabilities are aimed at being aligned with the European FI-PPP activities and take advantage of its flagship project FIWARE.

 Deliverables

List of deliverables.
Bonseyes video Websites, patent fillings, videos etc. 2020-04-08 11:02:54
Website Websites, patent fillings, videos etc. 2020-04-08 11:02:54
Bonseyes flyer Websites, patent fillings, videos etc. 2020-04-08 11:02:54
Initial Deep Learning Methods Other 2020-04-08 11:02:54
Demonstrator Proof of Concepts Documents, reports 2020-04-08 11:02:54
Initial Platform Deployment Methods and Tools Other 2020-04-08 11:02:54

Take a look to the deliverables list in detail:  detailed list of Bonseyes deliverables.

 Publications

year authors and title journal last update
List of publications.
2018 Turner, Jack; Crowley, Elliot J.; Radu, Valentin; Cano, José; Storkey, Amos; O\'Boyle, Michael
Distilling with Performance Enhanced Students
published pages: , ISSN: , DOI:
1 2020-04-08
2018 Crowley, Elliot J.; Gray, Gavin; Storkey, Amos
Moonshine: Distilling with Cheap Convolutions
published pages: , ISSN: , DOI:
Crowley , E , Gray , G & Storkey , A 2018 , Moonshine: Distilling with Cheap Convolutions . in Thirty-second Conference on Neural Information Processing Systems (NIPS 2018) . Montreal, Canada , Thirty-second Conference on Neural Information Processing Systems , Montreal , Canada , 3/12/18 . 1 2020-04-08
2018 Stanisław Jastrzębski, Zachary Kenton, Nicolas Ballas, Asja Fischer, Yoshua Bengio, Amos Storkey
https://arxiv.org/abs/1807.05031v1
published pages: , ISSN: , DOI:
2020-04-08
2019 Antoniou, Antreas; Storkey, Amos
Assume, Augment and Learn: Unsupervised Few-Shot Meta-Learning via Random Labels and Data Augmentation
published pages: , ISSN: , DOI:
1 2020-04-08
2018 Jastrzębski, Stanislaw; Kenton, Zachary; Ballas, Nicolas; Fischer, Asja; Bengio, Yoshua; Storkey, Amos
On the Relation Between the Sharpest Directions of DNN Loss and the SGD Step Length
published pages: , ISSN: , DOI:
Jastrzębski , S , Kenton , Z , Ballas , N , Fischer , A , Bengio , Y & Storkey , A 2019 , \' On the Relation Between the Sharpest Directions of DNN Loss and the SGD Step Length \' , Paper presented at Seventh International Conference on Learning Representations , New Orleans , United States , 6/05/19 - 9/05/19 . 1 2020-04-08
2018 Loukadakis, Manolis; Cano, Jose; O\'Boyle, Michael
Accelerating Deep Neural Networks on Low Power Heterogeneous Architectures
published pages: , ISSN: , DOI:
Loukadakis , M , Cano , J & O\'Boyle , M 2018 , Accelerating Deep Neural Networks on Low Power Heterogeneous Architectures . in 11th International Workshop on Programmability and Architectures for Heterogeneous Multicores (MULTIPROG-2018) . 11th International Workshop on Programmability and Architectures for Heterogeneous Multicores (MULTIPROG-2018) , Manchester , United Kingdom , 24/01/18 . 1 2020-04-08
2019 Gray, Gavin; Crowley, Elliot J.; Storkey, Amos
Separable Layers Enable Structured Efficient Linear Substitutions
published pages: , ISSN: , DOI:
1 2020-04-08
2018 Ahmadi Mehri, Vida; Ilie, Dragos; Tutschku, Kurt
Towards Privacy Requirements for Collaborative Development of AI Applications
published pages: , ISSN: , DOI:
1 2020-04-08
2019 de Prado, Miguel; Su, Jing; Dahyot, Rozenn; Saeed, Rabia; Keller, Lorenzo; Vallez, Noelia
AI Pipeline - bringing AI to you. End-to-end integration of data, algorithms and deployment tools
published pages: , ISSN: , DOI:
1 2020-04-08
2018 de Prado, Miguel; Pazos, Nuria; Benini, Luca
Learning to infer: RL-based search for DNN primitive selection on Heterogeneous Embedded Systems
published pages: , ISSN: , DOI:
1 2020-04-08
2018 Stanisław Jastrzębski, Zachary Kenton, Devansh Arpit, Nicolas Ballas, Asja Fischer, Yoshua Bengio, Amos Storkey
Three Factors Influencing Minima in SGD
published pages: , ISSN: , DOI:
2020-04-08
2019 Crowley, Elliot J.; Turner, Jack; Storkey, Amos; O\'Boyle, Michael
A Closer Look at Structured Pruning for Neural Network Compression
published pages: , ISSN: , DOI:
1 2020-04-08
2018 Anderson, Andrew; Gregg, David
Optimal DNN Primitive Selection with Partitioned Boolean Quadratic Programming
published pages: , ISSN: , DOI:
1 2020-04-08
2017 Vasudevan, Aravind; Anderson, Andrew; Gregg, David
Parallel Multi Channel Convolution using General Matrix Multiplication
published pages: , ISSN: , DOI:
arXiv.org e-Print Archive 1 2020-04-08
2017 Anderson, Andrew; Vasudevan, Aravind; Keane, Cormac; Gregg, David
Low-memory GEMM-based convolution algorithms for deep neural networks
published pages: , ISSN: , DOI:
2 2020-04-08
2018 Loukadakis, M., Cano, J. & O’Boyle, M.
Accelerating Deep Neural Networks on Low Power Heterogeneous Architectures.
published pages: , ISSN: , DOI:
11th International Workshop on Programmability and Architectures for Heterogeneous Multicores (MULTIPROG-2018). 11th International Workshop on Programmability and Architectures for Heterogeneous Multicores (MULTIPROG-2018), Manchester, United Kingdom, 24 January 2020-04-08
2017 Crowley, Elliot J.; Gray, Gavin; Storkey, Amos
Moonshine: Distilling with Cheap Convolutions
published pages: , ISSN: , DOI:
1 2020-04-08
2017 Mehri, Vida. A., Tutschku, Kurt
Flexible Privacy and High Trust in the Next Generation Internet - The Use Case of a Cloud-based Marketplace for AI
published pages: , ISSN: , DOI:
SNCNW - Swedish National Computer Networking Workshop, Halmstad 2020-04-08

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The information about "BONSEYES" are provided by the European Opendata Portal: CORDIS opendata.

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