ENergy Efficient Adaptive Computing with multi-grain heterogeneous architectures (ENEAC)

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Energy efficiency is one of the primary design constraints for modern processing systems. Hardware accelerators are seen as a key technology to address the high performance with limited energy issue. In addition the arrival of computing languages such as OpenCL offer a route to the programmer to target different types of multi-core accelerators using a single source code. Performance portability is a significant challenge specially if the accelerators have different microarchitectures such as is the case in CPU-GPU-FPGA systems. This research addresses the energy and performance challenge by investigating how a device formed by processing units with different granularities ranging from coarse grain CPU cores of different complexity, medium grain general purpose GPU cores and fine grain FPGA logic cells can be dynamically programmed. The challenge is to be able to program all these resources with a single programming model and create a run-time system that can automatically tune the software to the best execution resource from energy and performance points of view. The results from this research are expected to deliver new fundamental insights to the question of: How future computers can obtain orders of magnitude higher performance with limited energy budgets?