Explore the words cloud of the Bonseyes project. It provides you a very rough idea of what is the project "Bonseyes" about.
The following table provides information about the project.
Coordinator |
NVISO SA
Organization address contact info |
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 |
Take a look of project's partnership.
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.
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.
year | authors and title | journal | last update |
---|---|---|---|
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.