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International audienceEmerging trends in applications with the requirement of considerable computational performance and decreasing time-to-market have urged the need of multiprocessor systems. With the increase in number of processor there is an increased demand to efficiently control the energy and power budget of such embedded systems as well. Dynamic voltage frequency scaling (DVFS) strategies permits to control this budget by actively changing the power consumption profile of the system. These techniques exploit the execution times variation of Real-time applications for dynamically adjusting the voltage and frequency of processors in order to reduce power and energy consumption. This paper presents one DVFS strategy called Dynamic slack reclamation (DSR) for real time applications. It is based on detecting early completion of tasks. DSR determines the amount of dynamic slack (if any) by comparing the worst-case execution requirement of a tasks job with its actual execution requirements. Experimental results will be extracted from the simulation of MPSoC architectures using the SoCLib platform; they show that DSR approach gives better power consumption performance

Abstract. In this article, we present two studies that pave the way towards a mobile implementation of the WAAVES certified medical image compression encoder. On the algorithmic side, we compared three techniques to increase the compression rate. The obtained results show a significant bit-rate reduction, around 40% with respect to the WAAVES encoder, while keeping the same visual quality. On the architectural side, we describe the HW/SW co-design of an architecture implemented in a FPGA platform. By using code profiling, critical portions of the code were identified, then two methods for hardware acceleration were used to implement the critical part of the coder. The tests were done on a StratixIVGX230 FPGA and the results showed that HW/SW co-design could achieve up to 20x performance gain in the critical portion. The combination of these results demonstrates the feasibility of a mobile implementation of the WAAVES certified medical image coder suitable for e-health applications.