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首页> 外文期刊>Arabian Journal for Science and Engineering. Section A, Sciences >An Efficient Implementation of Divergence State Estimation with Biogeography-Based Optimization (DSEBBO) Framework in FPGA-Based Multiprocessor System
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An Efficient Implementation of Divergence State Estimation with Biogeography-Based Optimization (DSEBBO) Framework in FPGA-Based Multiprocessor System

机译：基于FPGA的多处理器系统中的基于生物地理的优化（DSEBBO）框架的发散状态估计有效实现

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摘要

In recent years, Field Programmable Gate Array (FPGA) has different, and novel combinations of soft and hard cores embedded with accelerators in the same chip. At present, "Heterogeneous Multiprocessor System-on-Chip" technology meets the needs of the FPGA architecture as it not only consumes less space but also its design is implemented to enhance the performance resulting in decreased power consumption. However, traditional approaches can work within a limited range of values incurring high-power consumption, and they need substantial hardware resources involving complex procedures. In our proposed work, the combination of Graph Theory Estimator and Divergence State Estimation with Biogeography- Based Optimization (DSEBBO) is introduced. The principle notion of this proposed scheme is to optimize the required area resources and power consumption of the overall architecture by exploring the design space in a minimal time. The architecture uses a less number of hardware resources with a better outcome. The performances such as the latency, power consumption, delay, area and data rate are achieved better than the traditional works, and it is also application-aware with a multitask system. While comparing the outcome with the conventional asymmetric heterogeneous MPSoC method, the new DSEBBO design exhibits the performance improvement of 26.87% in data rate, and 37.18% reduction in average power consumption and 63.61% reduction in delay analysis under various traffic scenarios.
机译：近年来，现场可编程门阵列（FPGA）具有不同的软硬芯的不同，嵌入同一芯片中的加速器的新组合。目前，“异构多处理器系统上片上”技术符合FPGA架构的需求，因为它不仅消耗了更少的空间，而且还实现了其设计以增强功耗降低导致的性能。然而，传统方法可以在有限范围内工作，这是一个导致高功耗的值，并且它们需要涉及复杂程序的大量硬件资源。在我们提出的工作中，介绍了图形理论估计和与生物地基优化（DSEBBO）的差异状态估计的组合。该拟议方案的原理概念是通过在最小时间内探索设计空间来优化整体架构所需的区域资源和功耗。该体系结构使用少量硬件资源，具有更好的结果。延迟，功耗，延迟，区域和数据速率的性能比传统的作品更好地实现，并且还可以使用多任务系统来应用。在将结果与传统的不对称异构MPSOC方法进行比较时，新的DSEBBO设计表现出26.87％的数据速率的性能提高，平均功耗降低37.18％，在各种交通场景下减少了63.61％。

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