Project description
A scalable, secure system for hardware-accelerated artificial intelligence
Data is collected all around us, in rapidly increasing volumes from countless sources. The question is how to extract valuable knowledge and commercial value from data. This requires novel methods, approaches and engineering paradigms in machine learning, analytics and data management. The EU-funded EVEREST project is developing a holistic approach for co-designing computation and communication in a heterogeneous, distributed, scaleable and secure system for high-performance Big Data analytics. It will simplify the programmability of heterogeneous and distributed architectures through a ‘data-driven’ design approach and by using hardware-accelerated artificial intelligence and a unified hardware/software paradigm. The project will validate its approach by applying it in real-life business scenarios such as a weather analysis-based prediction model and a smart city traffic modelling framework.
Objective
The distributed and heterogeneous nature of the data sources in High Performance Big Data Analytics (HPDA) applications, as well as the required computational power, is pushing designers towards novel computing systems that combine HPC, Cloud, and IoT solutions (for efficient and distributed computation closer to the data) with Artificial Intelligence (AI) algorithms (for knowledge extraction and decision making).
In this context, the EVEREST project addresses the matching problem between application (and data) requirements, and the characteristics of the underlying heterogeneous hardware. Only an optimal match leads to efficient computation. In particular, we forecast that the creation of future Big Data systems will be of course data-driven, but also featuring complex heterogeneous and reconfigurable architectures that must be redesigned or customized based on the nature and locality of the data, and the type of learning/decisions to be performed.
The EVEREST project aims at developing a holistic approach for co-designing computation and communication in a heterogeneous, distributed, scalable and secure system for HPDA. This is achieved by simplifying the programmability of heterogeneous and distributed architectures through a “data-driven” design approach, the use of hardware-accelerated AI, and through an efficient monitoring of the execution with a unified hardware/software paradigm. EVEREST proposes a design environment that combines state-of-the-art, stable programming models, and emerging communication standards, with novel and dedicated domain-specific extensions.
Three industry-relevant application scenarios are used to validate the EVEREST approach and act as business cases for the project exploitation: (i) a weather analysis-based prediction model for the renewable energy trading market, (ii) an application for air-quality monitoring of industrial sites, and (iii) a real-time traffic modeling framework for intelligent transportation in smart cities.
Fields of science
- natural sciencescomputer and information sciencesartificial intelligence
- engineering and technologycivil engineeringurban engineeringsmart cities
- natural sciencescomputer and information sciencesinternetinternet of things
- engineering and technologyenvironmental engineeringenergy and fuelsrenewable energy
- natural sciencescomputer and information sciencesdata sciencebig data
Keywords
Programme(s)
Funding Scheme
RIA - Research and Innovation actionCoordinator
8803 Rueschlikon
Switzerland