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An Embedded Software Energy Consumption Testing Method Based on Improved Neural Network

A technology of embedded software and testing methods, applied in the field of embedded software energy consumption testing, can solve problems such as the inability to accurately analyze software energy consumption, and achieve the effects of energy consumption optimization and code reuse rate improvement

Active Publication Date: 2017-12-22
KUNMING UNIV OF SCI & TECH
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Problems solved by technology

[0003] However, in the energy consumption test through neural network training, people only use the traditional neural network model to train the feature quantities in the software, but these feature quantities cannot accurately analyze the energy consumption of the software. consumption analysis and research

Method used

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  • An Embedded Software Energy Consumption Testing Method Based on Improved Neural Network
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  • An Embedded Software Energy Consumption Testing Method Based on Improved Neural Network

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Embodiment 1

[0027] Embodiment 1: as Figure 1-5 As shown, an embedded software energy consumption test method based on the improved neural network, firstly analyzes the introduced embedded software to obtain the node relationship graph; then according to the node relationship graph, analyzes the clustering relationship, out-degree, in- According to the clustering relationship of each node, the average value of the clustering relationship is obtained; then the clustering relationship, out-degree, in-degree value of the input node, and the average value of the clustering relationship obtained by the clustering relationship of each input node are substituted into the improved The neural network model of each output node; then calculate the error of the output value of each node according to the output value of each output node and the set training target value of each output node; finally, in the Linux system, the number of optimized nodes corresponds to The embedded software uses the power ...

Embodiment 2

[0042] Embodiment 2: as Figure 1-5 As shown, an embedded software energy consumption test method based on the improved neural network, firstly analyzes the introduced embedded software to obtain the node relationship graph; then according to the node relationship graph, analyzes the clustering relationship, out-degree, in- According to the clustering relationship of each node, the average value of the clustering relationship is obtained; then the clustering relationship, out-degree, in-degree value of the input node, and the average value of the clustering relationship obtained by the clustering relationship of each input node are substituted into the improved The neural network model of each output node; then calculate the error of the output value of each node according to the output value of each output node and the set training target value of each output node; finally, in the Linux system, the number of optimized nodes corresponds to The embedded software uses the power ...

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Abstract

The invention relates to an embedded software energy consumption testing method based on an improved neural network, and belongs to the field of embedded technology. The present invention firstly analyzes the imported embedded software to obtain a node relationship diagram; then analyzes the clustering relationship, out-degree, and in-degree of each node according to the node relationship diagram, and obtains the average value of the clustering relationship according to the clustering relationship of each node ; Then, the clustering relationship, out-degree, in-degree value of the input node, and the average value of the clustering relationship obtained by the clustering relationship of each input node are substituted into the improved neural network model; then according to the output value of each output node and the setting The training target value of each output node is determined, and the error of the output value of each node is calculated; finally, in the Linux system, the embedded software corresponding to the optimized number of nodes is tested for energy consumption with a power emulator, and the obtained Optimized energy consumption value. The invention improves code reuse rate and realizes energy consumption optimization.

Description

technical field [0001] The invention relates to an embedded software energy consumption testing method based on an improved neural network, and belongs to the field of embedded technology. Background technique [0002] Embedded software energy consumption is one of the hot issues in the current embedded field. In order to optimize software energy consumption, many testing and optimization methods have emerged. Energy consumption analysis is usually based on instruction-level analysis or at the algorithm and software architecture level. [0003] However, in the energy consumption test through neural network training, people only use the traditional neural network model to train the feature quantities in the software, but these feature quantities cannot accurately analyze the energy consumption of the software. consumption analysis and research. An embedded software energy consumption testing method for improved neural network, which analyzes the node relationship diagram in...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F11/36
Inventor 张晶陈沫良严涵沈江炎潘有顺薛冷王彬
Owner KUNMING UNIV OF SCI & TECH
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