Cyber-physical systems (CPS) consist of a variety of multicore architectures, including central processing units (CPU) and graphical processing units (GPU). In general, programmers assign sequential programs to the CPU while parallel applications are assigned to the GPU. This article provides a method for mapping an OpenCL application to a heterogeneous multicore architecture using active fuzzy learning to determine the adequacy and processing capabilities of the application. During learning, subsamples are created by developing a machine learning-based device suitability classifier that predicts which processors would have excessive computational compatibility for running OpenCL programs. In addition, this study integrates an active learning model based on entropy with a fuzzification model to find nonoverlapping patterns. To minimize rule generation, the fuzzification-based weighted probabilistic technique is presented. The defuzzification process is optimized by using uncertainty values in conjunction with classification probability. In addition, 20 different features are proposed for extraction using the newly developed LLVM-based static analyzer. The correlation analysis approach is used to determine the optimal subset of features. The synthetic minority oversampling approach with and without feature selection is used to differentiate the class imbalance problem. Instead of manually modifying the machine learning classifier, a tree-based pipeline construction approach is used to determine the optimal classifier and associated hyperparameters. Experiments are then conducted on a set of benchmarks to verify the performance of the designed model. The results show that by increasing the number of training examples and including an entropy uncertainty measure, the proposed model is able to support and improve decision boundaries. We achieved a high F-measure of 0.77 and a ROC of 0.92 by optimizing and reducing the feature subsets.

中文翻译：

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模糊主动学习检测网络物理系统中的 OpenCL 内核异构机器


信息物理系统（CPS）由各种多核架构组成，包括中央处理单元（CPU）和图形处理单元（GPU）。一般来说，程序员将顺序程序分配给CPU，而并行应用程序分配给GPU。本文提供了一种使用主动模糊学习将 OpenCL 应用程序映射到异构多核架构的方法，以确定应用程序的充分性和处理能力。在学习过程中，通过开发基于机器学习的设备适用性分类器来创建子样本，该分类器可以预测哪些处理器对于运行 OpenCL 程序具有过度的计算兼容性。此外，本研究将基于熵的主动学习模型与模糊化模型相结合，以发现不重叠的模式。为了最小化规则生成，提出了基于模糊化的加权概率技术。通过使用不确定性值和分类概率来优化去模糊化过程。此外，还提出了使用新开发的基于 LLVM 的静态分析器提取 20 个不同的特征。相关性分析方法用于确定特征的最佳子集。使用有和没有特征选择的合成少数过采样方法来区分类别不平衡问题。不是手动修改机器学习分类器，而是使用基于树的管道构建方法来确定最佳分类器和相关的超参数。然后在一组基准上进行实验，以验证设计模型的性能。 结果表明，通过增加训练样本的数量并包括熵不确定性度量，所提出的模型能够支持和改进决策边界。通过优化和减少特征子集，我们实现了 0.77 的高 F 测量和 0.92 的 ROC。