The Bonseyes project aims to develop a platform consisting of a Data Marketplace, Deep Learning Toolbox, and Developer Reference Platforms for organisations 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 organisations who lack access to Data and Models.

Overall, the project has progressed beyond the start-of-the-art in distributed and decentralised AI systems-of-systems development through:

  • Reduction in development time by 50% compared to monolithic system design methods, through the reuse of data, meta data, and models among separate legal entities made possible by the Bonseyes AI Marketplace, a data marketplace that modularizes the AI systems’ development value-chain.
  • Reduction in cost of ownership by a factor of 5 related to training of deep learning models compared to current training approaches designed for the cloud. Deep learning training methods for resource constrained devices that enable models with near state-of-the-art accuracy that are tailored for embedded, constrained, distributed systems operating in real environments with noisy, sometimes missing data.
  • Enabling distributed deep learning, where part of the training can be achieved in embedded devices themselves, partially alleviating the need to transmit vast amounts of labelled data back to the cloud. This step is a key enabler for eventual unsupervised learning on mobile devices.
  • Predictive tools that automate optimal on-device deployment of a model on the target embedded system given a specific power/space/time constraint. Enabling low power “always-on” intelligence on edge devices. Tools optimized for various low power universal reference developer platforms supporting “always-on” intelligence paradigms.
  • Privacy and data isolation for deep learning methods that can be selected and adapted by the data providers or model owners. Robust data collection tools for IoT devices scalable to a very large number of devices and maintain privacy and data isolation, while enabling real-time data processing on the “edge” device to identify when data is “abnormal”.

Learn more:

  • Start date: 1 December 2016
  • PI: Rozenn Dahyot - Partner
  • Acronym: BONSEYES
  • Title: Platform for Open Development of Systems of Artificial Intelligence
  • Website:
  • Grant ID: 732204
  • Overall budget: €8,593,952,50
Project Contact
  • Rozenn Dahyot