Combined MPSoC Task Mapping and Memory Optimization for Low-Power

Strobel, Manuel; Führ, Gereon; Radetzki, Marian; Leupers, Rainer

doi:10.1109/apccas47518.2019.8953133

Cited by 3 publications

(1 citation statement)

References 15 publications

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“…The future multi-processor system-on-chip (MPSoC) has hundreds or thousands of core resources, and the development trend of MPSoC will further develop towards the direction of heterogeneous multicore low power consumption and high performance. Especially the realization of efficient parallel computation on multi-core devices with limited or even no run-time environment has been subject to intensive research [1]. Solutions related to tool flow, parallelization language, software task mapping, and code generation for multiple targets are already commercially available [2].…”

Section: Introductionmentioning

confidence: 99%

Design and Implementation of a Dynamic Task Mapping Algorithm Based on Hotspot Statistics

Wang

Zhang

et al. 2022

J. Phys.: Conf. Ser.

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In a coarse-grained heterogeneous multi-core system, the task map is responsible for allocating the cores for task execution. The existing user static mapping method has low programming friendliness. As the scale and density of system tasks increase, so does the complexity of manual programming by users, resulting in programming inefficiencies. Aiming at this problem, based on the existing heterogeneous multi-core platform, this paper studies a dynamic task mapping method with hotspot statistics, and uses the preprocessing of the mapping to reduce the time spent on the actual mapping. The hardware mapper is designed and implemented in Verilog, and the verification platform is built in the HDL environment. The experimental results show that the new mapping algorithm significantly improves the performance of the system, with an average of 32.50% and a maximum of 47.16%.

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Section: Introductionmentioning

confidence: 99%