Machine learning based online mapping approach for heterogeneous multi-core processor system
AN Xin1,2, ZHANG Ying1,2, KANG An1,2, CHEN Tian1,2, LI Jianhua1,2
1. School of Computer and Information, Hefei University of Technology, Hefei Anhui 230601, China; 2. Anhui Provincial Key Laboratory of Affective Computing and Advanced Intelligent Machine(Hefei University of Technology), Hefei Anhui 230601, China
Abstract：Heterogeneous Multi-core Processors (HMPs) platform has become the mainstream solution for modern embedded system design, and online mapping or scheduling plays a vital role in making full use of the advantages of high performance and low power consumption. Aiming at the dynamic mapping problem of application tasks in HMPs, a mapping and scheduling approach based on machine learning prediction model was proposed. On the one hand, a machine learning model was constructed to predict and evaluate the performance of different mapping strategies rapidly and efficiently, so as to provide support for online scheduling. On the other hand, the machine learning model was integrated with genetic algorithm to find out the optimal resource allocation strategy efficiently. Finally, an Motion-Join Photographic Experts Group (M-JPEG) decoder was used to verify the effectiveness of the proposed approach. The experimental results show that, compared with the Round Robin Scheduler (RRS) and sampling scheduling approaches, the proposed online mapping/scheduling approach has reduced the average execution time by about 19% and 28% respectively.
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2019, Vol. 39 
