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Academic Journal of Engineering and Technology Science, 2024, 7(2); doi: 10.25236/AJETS.2024.070221.

Energy Consumption Analysis of Convolutional Neural Networks


Xiaokun Qi, Tian He

Corresponding Author:
Tian He

College of Mechanical and Electrical Engineering, Qingdao University, Qingdao, 266071, China


The rapid development and widespread application of convolutional neural networks have played a positive role in promoting social progress and economic development. An increasing number of neural networks are being deployed on mobile devices to meet the needs of visual perception tasks. However, the operation of complex neural networks requires a large amount of computational cost, which affects the working duration of mobile devices. Therefore, it becomes crucial to analyze the energy consumption of neural networks. Through energy consumption analysis, the main energy consumption points can be identified, providing ideas for the subsequent low-energy consumption of neural networks. This paper selects six classic convolutional neural networks, AlexNet, VGG, GoogLeNet, ResNet, MobileNet, and ShuffleNet, to study the impact of different network structures on energy consumption, and compares their running time, power, and energy consumption. On this basis, the hotspot layers (convolutional layer, pooling layer, fully connected layer) are analyzed, and it is found that the energy consumption of the convolutional layer accounts for the largest proportion. It is inferred from this that the internal structure of the convolutional layer is a key factor affecting the energy consumption of neural networks. Based on this, improvements to the internal structure of the convolutional layer can reduce algorithm energy consumption.


Convolutional Neural Networks, Hotspot Layer, Energy Consumption Analysis

Cite This Paper

Xiaokun Qi, Tian He. Energy Consumption Analysis of Convolutional Neural Networks. Academic Journal of Engineering and Technology Science (2024) Vol. 7, Issue 2: 144-149. https://doi.org/10.25236/AJETS.2024.070221.


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