A Genetic Algorithm for Scheduling in Heterogeneous Multicore System Integrated with FPGA

The heterogeneous multicore system integrated with FPGA is a kind of Multi-Processor Systems-on-Chip(MPSoC) that can achieve both efficiency and flexibility. FPGA uses hardware resource instead of instruction sets to process tasks. The resource on FPGA is limited which should be considered when scheduling tasks. We propose a static task scheduling algorithm targeting this kind of system. The aim is to minimize the executing time of an application as well as considering FPGA resource limit. In our genetic algorithm-based method, a chromosome consists of computing units where tasks are assigned. When generating the initial population, some tasks are assigned to FPGA, considering FPGA resource limit. We have modified the crossover operator and mutation operator to ensure that the FPGA resource used implied in chromosomes will not exceed the FPGA resource limit. Task scheduling is completed in the chromosome evaluation stage. Through the genetic algorithm, the improved task assignment and schedule sequence are obtained. The experiments on random graph applications and two real-world applications show that our method has achieved better performance than existing works.