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Pods - A novel intelligent energy efficient and dynamic frequency scalings for multi-core embedded architectures in an IoT environment

机译：Pods-用于物联网环境中多核嵌入式架构的新型智能节能和动态频率缩放

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In the Advent of the Internet of Things (IoT), embedded architecture takes an important dimension in terms of energy and accomplishment. The embedded system needs more and more intelligent algorithms for better performance and energy efficiency to fit into an IoT scenario. Moreover, with the existence of high-performance multi-core embedded architectures, achievements of energy efficiency remains in the dark side of the research. Several algorithms such as dynamic frequency scaling, thread mapping, starvation methodologies were proposed in embedded architectures for efficient usages of clock frequencies and these features were used as the energy saving modes in which the consumption of energy in the embedded architectures are being controlled. But these methods have several backlogs which permits the use of consumption in the embedded architectures. Considering the above features, this paper proposes a new methodology PODS(Predictors for Optimized Dynamic Scaling) which integrates a powerful machine learning algorithm for scaling the clock frequencies by the input workloads and allocation of the core depending based on the workload. The proposed framework PODS has different phases of working namely workload extraction, characterization, and optimization using BAT algorithms and prediction extreme Machine - Learning. The algorithm was tested on ARM/Cortex architectures (Raspberry Pi 3 Model B+), an evaluated algorithm using the IoMT benchmarks and various parameters that include energy consumption, accuracy of detection/prediction was determined and analyzed. It is found that the implementation of the proposed framework in the test is seen resulting between 35 and 40% reduction in the consumption of the power. (C) 2019 Elsevier B.V. All rights reserved.
机译：在物联网（IoT）的问世中，嵌入式架构在能量和成就方面占据着重要的位置。嵌入式系统需要越来越多的智能算法来实现更好的性能和能效，以适应IoT场景。此外，随着高性能多核嵌入式体系结构的存在，能源效率的成就仍处于研究的黑暗面。为了有效利用时钟频率，在嵌入式体系结构中提出了几种算法，例如动态频率缩放，线程映射，饥饿方法，并且这些特征被用作节能模式，在该节能模式下，嵌入式体系结构中的能量消耗得到控制。但是这些方法有很多积压，允许在嵌入式体系结构中使用消耗量。考虑到上述特征，本文提出了一种新的方法PODS（优化动态缩放预测器），该方法集成了功能强大的机器学习算法，可通过输入工作负载和根据工作负载分配内核来缩放时钟频率。所提出的PODS框架具有工作的不同阶段，即使用BAT算法和预测极限机器学习进行工作负载提取，表征和优化。该算法在ARM / Cortex架构（Raspberry Pi 3 Model B +）上进行了测试，使用IoMT基准和各种参数（包括能耗，检测/预测准确性）对算法进行了评估。发现在测试中可以看到拟议框架的实施，从而使功耗降低了35％至40％。 （C）2019 Elsevier B.V.保留所有权利。

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