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Optimizing energy and throughput for MPSoCs: an integer particle swarm optimization approach

机译:优化MPSoC的能量和吞吐量:整数粒子群优化方法

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Most of recent research in multicore processor architectures has been shifted towards reconfigurable architectures due to increasing complexity of computing systems. These systems provide better application-specific energy and throughput balance with their reconfigurable behavior. They perform automatic run time resource allocation for an application as per its needs. But in terms of performance, current methodologies produce some unpredictable results because of the actual variety of the workloads. Therefore, we need optimization of the system resources usage by employing some optimization algorithms. Early research in the field of reconfigurable architecture using optimization algorithms has produced efficient results for energy consumption with the reconfiguration of cache sizes and associativity, number of cores and operating frequency. In this research, we propose particle swarm optimization (PSO) based algorithm, Integer PSO (IPSO) for design space exploration of reconfigurable computer architectures to have better energy and throughput balance. The results obtained by IPSO are evaluated by using various SPLASH-2 benchmark applications. Evaluation shows notable reduction in energy consumption without major effect on throughput. Simulation results also support the use of IPSO in design space exploration of multicore reconfigurable processor architectures.
机译:由于计算系统的复杂性增加,最近在多核处理器体系结构上的大多数研究已转向可重配置体系结构。这些系统凭借其可重新配置的行为提供了更好的特定于应用程序的能量和吞吐量平衡。它们根据应用程序的需要为应用程序执行自动运行时资源分配。但是就性能而言,由于工作负载的实际多样性,当前的方法会产生一些无法预测的结果。因此,我们需要通过使用一些优化算法来优化系统资源的使用。使用优化算法的可重构体系结构领域的早期研究通过重新配置高速缓存大小和关联性,内核数和工作频率,产生了有效的能耗结果。在这项研究中,我们提出了基于粒子群优化(PSO)的算法,即整数PSO(IPSO),用于可重构计算机体系结构的设计空间探索,以具有更好的能量和吞吐量平衡。通过使用各种SPLASH-2基准应用程序来评估IPSO获得的结果。评估表明,能耗显着降低,而对吞吐量没有重大影响。仿真结果还支持IPSO在多核可重配置处理器体系结构的设计空间探索中的使用。

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