期刊文献+

Effective Task Scheduling for Embedded Systems Using Iterative Cluster Slack Optimization

Effective Task Scheduling for Embedded Systems Using Iterative Cluster Slack Optimization
下载PDF
导出
摘要 To solve computationally expensive problems, multiple processor SoCs (MPSoCs) are frequently used. Mapping of applications to MPSoC architectures and scheduling of tasks are key problems in system level design of embedded systems. In this paper, a cluster slack optimization algorithm is described, in which the tasks in a cluster are simultaneously mapped and scheduled for heterogeneous MPSoC architectures. In our approach, the tasks are iteratively clustered and each cluster is optimized by using the branch and bound technique to capitalize on slack distribution. The proposed static task mapping and scheduling method is applied to pipelined data stream processing as well as for batch processing. In pipelined processing, the tradeoff between throughput and memory cost can be exploited by adjusting a weighting parameter. Furthermore, an energy-aware task mapping and scheduling algorithm based on our cluster slack optimization is developed. Experimental results show improvement in latency, throughput and energy. To solve computationally expensive problems, multiple processor SoCs (MPSoCs) are frequently used. Mapping of applications to MPSoC architectures and scheduling of tasks are key problems in system level design of embedded systems. In this paper, a cluster slack optimization algorithm is described, in which the tasks in a cluster are simultaneously mapped and scheduled for heterogeneous MPSoC architectures. In our approach, the tasks are iteratively clustered and each cluster is optimized by using the branch and bound technique to capitalize on slack distribution. The proposed static task mapping and scheduling method is applied to pipelined data stream processing as well as for batch processing. In pipelined processing, the tradeoff between throughput and memory cost can be exploited by adjusting a weighting parameter. Furthermore, an energy-aware task mapping and scheduling algorithm based on our cluster slack optimization is developed. Experimental results show improvement in latency, throughput and energy.
出处 《Circuits and Systems》 2013年第8期479-488,共10页 电路与系统(英文)
关键词 MULTI-PROCESSOR Mapping Scheduling Multi-Processor Mapping Scheduling
  • 相关文献

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部