Energy consumption optimization scheduling method for heterogeneous multi-core embedded systems based on reinforcement learning

Abstract

The invention discloses a heterogeneous multi-core embedded system energy consumption optimization scheduling method based on a reinforcement learning algorithm. In the hardware aspect, a DVFS regulator is loaded on each processor, and the hardware platform matching the software characteristics is dynamically constructed by adjusting the working voltage of each processor and changing the hardwarecharacteristics of each processor. In the aspect of software, aiming at the shortcomings of traditional heuristic algorithm (genetic algorithm, annealing algorithm, etc.), such as insufficient local searching ability or weak global searching ability, this paper makes an exploratory application of Q-Learning algorithm to find the optimal scheduling solution of energy consumption. The Q-Learning algorithm can give consideration to the performance of global search and local search by trial-and-error and interactive feedback with the environment, so as to achieve better search results than the traditional heuristic algorithm. Thousands of experiments show that compared with the traditional GA algorithm, the energy consumption reduction rate of the Q-learning algorithm can reach 6%-32%.

Description

technical field [0001] The invention belongs to the technical field of parallel and distributed systems, and relates to a method for optimizing energy consumption of a heterogeneous multi-core embedded system, in particular to a method for optimizing energy consumption of a heterogeneous multi-core embedded system based on a reinforcement learning Q-Learning algorithm. technical background [0002] With the rapid development of electronic technology, applications with high computational complexity, such as image processing, high-definition television, and electronic games, are gradually applied to embedded devices, and higher and higher requirements are placed on the performance of embedded devices. The performance of embedded devices can be improved by increasing the main frequency, however, the increase of the main frequency will rapidly increase the energy consumption of the processor ([Document 1]), thus shortening the working life of embedded devices. In order to optimi...

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Problems solved by technology

However, traditional heuristic algorithms have disadvantages such as insufficient local search ability or easy to fall into local optimum. excellent

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