Outcomes of the Bonseyes project

Scientific publications

Publications in peer-reviewed journals, conference proceedings, book chapters and books.
1.
Towards Privacy Requirements for Collaborative Development of AI Applications.
in 14th Swedish National Computer Networking Workshop (SNCNW), 2018 (2018). http://urn.kb.se/resolve?urn=urn:nbn:se:bth-16446.
2.
Optimal DNN primitive selection with partitioned boolean quadratic programming.
in Proceedings of the 2018 International Symposium on Code Generation and Optimization - CGO 2018 340-351 (ACM Press, 2018). doi:10.1145/3168805. Archive: arXiv.org
3.
Accelerating Deep Neural Networks on Low Power Heterogeneous Architectures.
in 11th International Workshop on Programmability and Architectures for Heterogeneous Multicores (MULTIPROG-2018) (2018). Archive: Semantic Scholar
4.
Three Factors Influencing Minima in SGD.
arXiv:1711.04623 [cs, stat] (2017). Archive: arXiv.org
5.
Moonshine: Distilling with Cheap Convolutions.
arXiv:1711.02613 [cs, stat] (2017). Archive: arXiv.org
6.
Low-memory GEMM-based convolution algorithms for deep neural networks.
arXiv:1709.03395 [cs] (2017). Archive: arXiv.org
7.
Pricing of Data Products in Data Marketplaces.
in Software Business 49-66 (Springer, Cham, 2017). doi:10.1007/978-3-319-69191-6_4. Archive: DIVA
8.
Performance Analysis and Optimization of Sparse Matrix-Vector Multiplication on Modern Multi- and Many-Core Processors.
in 292-301 (IEEE, 2017). doi:10.1109/ICPP.2017.38. Archive: arXiv.org
9.
BONSEYES: Platform for Open Development of Systems of Artificial Intelligence: Invited Paper.
in Proceedings of the Computing Frontiers Conference 299–304 (ACM, 2017). doi:10.1145/3075564.3076259
10.
Privacy and trust in cloud-based marketplaces for AI and data resources.
in 223-225 (Springer New York LLC, 2017). doi:10.1007/978-3-319-59171-1
11.
Flexible Privacy and High Trust in the Next Generation Internet : The Use Case of a Cloud-based Marketplace for AI.
in DIVA (Halmstad university, 2017). http://urn.kb.se/resolve?urn=urn:nbn:se:bth-14963.
12.
Parallel Multi Channel convolution using General Matrix Multiplication.
in 19-24 (IEEE, 2017). doi:10.1109/ASAP.2017.7995254. Archive: arXiv.org

Selected presentations

Presentations at major conferences and public events
May
18
"Data >< Intelligence" . Keynote at Zooming Innovation in Consumer Electronics International Conference 2018 (ZINC 2018), Novi Sad, Serbia, 30 Mai 2018.
Mar
18
"BONSEYES: The artificial intelligence marketplace Supporting Surgical Data Science". DGE-BV 2018, Munich, Germany, 17 March 2018.
Dec
17
"Bonseyes AI Marketplace for Secure and Distributed Artificial Intelligence". School of Computer Science and Engineering at the University of New South Wales, Sydney, Australia, 14 December 2017.
Dec
17
"The Hardware and Software that will bring Deep Learning Everywhere" . Manchester, UK EMiT@CIUK workshop, 13 December 2017.
Nov
17
"DRM and Privacy in Virtualised and Programmable Network Architectures and Functions – The Bonseyes Use Case". Keynote at the Fourth Workshop on Network Function Virtualization and Programmable Networks (co-located with the 2017 IEEE Conference on Network Function Virtualization and Software Defined Networks (IEEE NFV-SDN 2017), Berlin, 6 November 2017.
Sep
17
"Artificial Intelligence: Mysteries of Emotions". ICCE Berlin 2017, Germany, 5 September 2017.
Jun
17
"Hybrid and Flexible Computing Architectures for Deep Learning Systems". Keynote at Zooming Innovation in Consumer Electronics International Conference 2017 (ZINC 2017), Novi Sad, Serbia. 31 May – 1 June 2017.
May
17
"BONSEYES: Platform for Open Development of Systems of Artificial Intelligence". ACM International Conference on Computing Frontiers 2017. 15–17 May, 2017, Siena, Italy.

Project Flyer

The Bonseyes Project flyer is mainly intended as a printed product but its electronic version is available for download.

Videos

Introductory videos to present the project, its goals and its partners.
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 732204 (Bonseyes). This work is supported by the Swiss State Secretariat for Education‚ Research and Innovation (SERI) under contract number 16.0159. The opinions expressed and arguments employed herein do not necessarily reflect the official views of these funding bodies.
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