Projet APROPOS (Horizon 2020, appel à projets 2020)
Action : Doctoral Networks
Panel : Information Science and Engineering
Statut du projet : SIGNED

The Approximate Computing for Power and Energy Optimisation ETN will train 15 ESRs to tackle the challenges of future embedded and high-performance computing energy efficiency by using disruptive methodologies. Following the current trend, by 2040 computers will need more electricity than the world energy resources can generate. On the communications side, energy consumption in mobile broadband networks is comparable to datacenters. To make things worse, Internet-of-Things will soon connect up to 50 billion devices through wireless networks to the cloud. APROPOS aims at decreasing energy consumption in both distributed computing and communications for cloud-based cyber-physical systems. We propose adaptive Approximate Computing to optimize energy-accuracy trade-offs. Luckily, in many parts of the global data acquisition, transfer, computation, and storage systems there exists the possibility to trade off accuracy to less power and less time consumed. As examples, numerous sensors are measuring noisy or inexact inputs; the algorithms processing the acquired signals can be stochastic; the applications using the data may be satisfied with an “acceptable” accuracy instead of exact and absolutely correct results; the system may be resilient against occasional errors; and a coarse classification may be enough for a data mining system. By introducing a new dimension, accuracy, to the design optimization, the energy efficiency can even be improved by a factor of 10x-50x. We will train the spearheads of the future generation to cope with the technologies, methodologies, and tools for successfully applying Approximate Computing to power and energy saving. The training, in this first ever ITN addressing approximate computing, is to a large extent done by researching energy-accuracy trade-offs on circuit, architecture, software, and system-level solutions, bringing together world leading experts from European organizations to train the ESR fellows.

Mots clés : approximate computing, energy efficiency, wearables, edge computing, cloud computing, arithmetics, approximation, stochastic computing, hardware, software, computing system

Participants :
- Politecnico di Milano (Italie) — Partner, beneficiary, Enseignement supérieur ou secondaire, subvention : 261499.68 €
- University of Bologna (Italie) — Partner, beneficiary, Enseignement supérieur ou secondaire, subvention : 261499.68 €
- KTH Royal Institute of Technology (Suède) — Partner, beneficiary, Enseignement supérieur ou secondaire, subvention : 281982.96 €
- Centrale Lyon (France) — Partner, beneficiary, Enseignement supérieur ou secondaire, subvention : 274802.04 €
- Delft University of Technology (Pays-Bas) — Partner, beneficiary, Enseignement supérieur ou secondaire, subvention : 265619.88 €
- University of Amsterdam (Pays-Bas) — Partner, beneficiary, Enseignement supérieur ou secondaire, subvention : 265619.88 €
- TU Wien (Autriche) — Partner, beneficiary, Enseignement supérieur ou secondaire, subvention : 264207.24 €
- University of Turku (Finlande) — Partner, beneficiary, Enseignement supérieur ou secondaire, subvention : 280805.76 €
- Queen's University Belfast (Royaume-Uni) — Partner, beneficiary, Enseignement supérieur ou secondaire, subvention : 303172.56 €
- Polytechnic University of Turin (Italie) — Partner, beneficiary, Enseignement supérieur ou secondaire, subvention : 261499.68 €
- Tampere University (Finlande) — Coordinator, beneficiary, Enseignement supérieur ou secondaire, subvention : 561611.52 €
- IBM Research - Zurich (Suisse) — Partner, beneficiary, Organismes privés (hors enseignement), subvention : 281276.64 €
- Wirepas oy (Finlande) — Partner, beneficiary, Organismes privés (hors enseignement), subvention : 280805.76 €
- Universitat Politècnica de València (Espagne) — Partner, beneficiary, Enseignement supérieur ou secondaire, subvention : 250904.88 €