Self-Adaptive Virtualisation-Aware High-Performance/Low-Energy Heterogeneous System Architectures
SAVE aims at the development of software/hardware technologies for an efficient exploitation of heterogeneous system architectures.
The SAVE project aims at exploiting self-adaptivity and hardware-assisted virtualisation to allow the system to autonomously decide which specialised computing resources are exploited to achieve a more efficient execution based on user-defined optimisation goals, such as performance, energy, reliability.
R & D innovation
SAVE will define crosscutting SW/HW technologies for implementing self-adaptive systems exploiting GPUs and FPGA-based dataflow engines (DFEs) that enhance heterogeneous architectures to cope with the increased variety and dynamics of high-performance and embedded computing workloads. Virtualisation and self-adaptation are jointly exploited to obtain a new self-adaptive virtualisation-aware Heterogeneous System Architecture (saveHSA). This architecture exhibits a highly dynamic behaviour to achieve the requested performance while minimising energy consumption allocating tasks to the most appropriate computing resources.
Industrial relevance / Potential applications and future issues
The project brings together key European technology companies (ST Microelectronics and ARM) and high-potential SMEs (Maxeler and Virtual Open Systems) who see a timely opportunity to develop self-adaptation and virtualisation technologies to deliver a new generation of HSAs. These architectures will be a passkey for entering the market with innovative solutions with improved cost/performance/energy/resilience characteristics, reinforcing the competitiveness of those four European technology suppliers across the computing spectrum. On the academic side, Politecnico di Milano (coordinating the project), the Technological Educational Institute of Crete and the University of Paderborn will contribute to the highly innovative research activities.
Type:EU FP7 STREP
Duration:September 2013 – August 2016
Research area:Advanced Computing, Embedded and Control Systems “Exploiting synergies and strengths between computing segments”