Toward Emotion-Aware Computing: A Loop Selection Approach Based on Machine Learning for Speculative Multithreading

Liu, Bin; He, Jinrong; Geng, Yaojun; Huang, Lu-Qi; Li, Shuqin

doi:10.1109/access.2017.2684129

Cited by 7 publications

(6 citation statements)

References 27 publications

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“…In computational theory, the simplest computational resources are computation time, the number of parameters necessary to solve a problem, and memory space [28]. In this section, the computational resource comparisons of four classic neural network models and the proposed model are analyzed in Table 6.…”

Section: Computational Resourcesmentioning

confidence: 99%

Identification of Apple Leaf Diseases Based on Deep Convolutional Neural Networks

et al. 2017

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Mosaic, Rust, Brown spot, and Alternaria leaf spot are the four common types of apple leaf diseases. Early diagnosis and accurate identification of apple leaf diseases can control the spread of infection and ensure the healthy development of the apple industry. The existing research uses complex image preprocessing and cannot guarantee high recognition rates for apple leaf diseases. This paper proposes an accurate identifying approach for apple leaf diseases based on deep convolutional neural networks. It includes generating sufficient pathological images and designing a novel architecture of a deep convolutional neural network based on AlexNet to detect apple leaf diseases. Using a dataset of 13,689 images of diseased apple leaves, the proposed deep convolutional neural network model is trained to identify the four common apple leaf diseases. Under the hold-out test set, the experimental results show that the proposed disease identification approach based on the convolutional neural network achieves an overall accuracy of 97.62%, the model parameters are reduced by 51,206,928 compared with those in the standard AlexNet model, and the accuracy of the proposed model with generated pathological images obtains an improvement of 10.83%. This research indicates that the proposed deep learning model provides a better solution in disease control for apple leaf diseases with high accuracy and a faster convergence rate, and that the image generation technique proposed in this paper can enhance the robustness of the convolutional neural network model.

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Section: Computational Resourcesmentioning

confidence: 99%

Identification of Apple Leaf Diseases Based on Deep Convolutional Neural Networks

et al. 2017

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“…Compared with the method that combines static compilation benefit analysis and a dynamic operating loop scheduling scheme [31], the proposed method can obtain a higher parallelism granularity. Compared with the multilevel nested loop parallelization model proposed by Liu et al [32] based on the KNN algorithm of machine learning, the proposed method is simpler and more intuitive, which makes it easier to integrate with mainstream compilers. Given that Dice et al [9] considered the actual running state of the multilevel nested loops to be closely related to the underlying hardware and that Li et al [2] extracted the relevant parameters of multilevel nested loops from the intermediate representation of the Prophet compiler, which relies on the organizational structure of the specific compiler, the proposed method starts from the direction matrix of multilevel nested loops and thus has a higher applicability in mainstream compilers.…”

Section: Discussionmentioning

confidence: 99%

“…We selected two loop selection methods, Method A and Method B, which have been mostly investigated. Method A uses a loop selection method that combines the compile time and the runtime [31], and Method B uses a loop selection method based on machine learning [32]. e automatic parallelization performance of the basic compiler was again used as the benchmark of the test.…”

Section: Comparison Of the Proposed Methods With Existingmentioning

confidence: 99%

“…erefore, the methods described in these references lack generality [31,[38][39][40][41]. As reported in [32,[42][43][44] the relevant features, such as the number of loop iterations of multilevel nested loops and the number of nested layers, were extracted from the intermediate representation of the compiler to construct the loop selection assessment model of multilevel nested loops. Compared with the models based on the thread-level speculation technique, these models improve the parallelization effect of multilevel nested loops to some extent.…”

Section: Related Workmentioning

confidence: 99%

“…Compared with the models based on the thread-level speculation technique, these models improve the parallelization effect of multilevel nested loops to some extent. However, as the number of nested loop levels of the program increases, the iteration dependency between loops becomes complicated, which makes them still unable to achieve the desired performance improvement [32,[42][43][44]. e authors in [45][46][47] proposed frameworks for misspeculation based on the loop cost in the compiler.…”

Section: Related Workmentioning

confidence: 99%

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Loop Selection for Multilevel Nested Loops Using a Genetic Algorithm

Nie

Zhou

Huang

et al. 2021

Mathematical Problems in Engineering

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Loop selection for multilevel nested loops is a very difficult problem, for which solutions through the underlying hardware-based loop selection techniques and the traditional software-based static compilation techniques are ineffective. A genetic algorithm- (GA-) based method is proposed in this study to solve this problem. First, the formal specification and mathematical model of the loop selection problem are presented; then, the overall framework for the GA to solve the problem is designed based on the mathematical model; finally, we provide the chromosome representation method and fitness function calculation method, the initial population generation algorithm and chromosome improvement methods, the specific implementation methods of genetic operators (crossover, mutation, and selection), the offspring population generation method, and the GA stopping criterion during the GA operation process. Experimental tests with the SPEC2006 and NPB3.3.1 standard test sets were performed on the Sunway TaihuLight supercomputer. The test results indicated that the proposed method can achieve a speedup improvement that is superior to that by the current mainstream methods, which confirm the effectiveness of the proposed method. Solving the loop selection problem of multilevel nested loops is of great practical significance for exploiting the parallelism of general scientific computing programs and for giving full play to the performance of multicore processors.

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