A Game-Theoretic Approach for Run-Time Distributed Optimization on MP-SoC

With forecasted hundreds of processing elements (PEs), future embedded systems will be able to handle multiple applications with very diverse running constraints. Systems will integrate distributed decision capabilities. In order to control the power and temperature, dynamic voltage frequency scalings (DVFSs) are applied at PE level. At system level, it implies to dynamically manage the different voltage/frequency couples of each tile to obtain a global optimization. This paper introduces a scalable multiobjective approach based on game theory, which adjusts at run-time the frequency of each PE. It aims at reducing the tile temperature while maintaining the synchronization between application tasks. Results show that the proposed run-time algorithm requires an average of 20 calculation cycles to find the solution for a 100-processor platform and reaches equivalent performances when comparing with an offline method. Temperature reductions of about 23% were achieved on a demonstrative test-case.