Optimization study of convolutional neural network model based on computer simulation context

Xuxu Zhang; Jikai Hua

doi:10.1117/12.3012560

21 December 2023 Optimization study of convolutional neural network model based on computer simulation context

Xuxu Zhang, Jikai Hua

Proceedings Volume 12970, Fourth International Conference on Signal Processing and Computer Science (SPCS 2023); 129703A (2023) https://doi.org/10.1117/12.3012560
Event: Fourth International Conference on Signal Processing and Computer Science (SPCS 2023), 2023, Guilin, China

Abstract

Convolutional Neural Network (CNN) is the most successful mathematical model of an artificial neural network used in the fields of computer vision, image recognition, and classification. High-performance neural network structures are large in size, and a large number of multiplicative-additive calculations are required for a complete inference process. Training out a high-performance convolutional neural network model requires tens of times more computation than the inference process. The current theoretical and technical level cannot achieve a model that can be universally applied to all domains, and different application scenarios require designing specific neural network structures and collecting specific data sets. Large computing power requirements and high-quality data acquisition are two key components for training to obtain high-performance convolutional neural networks.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Xuxu Zhang and Jikai Hua "Optimization study of convolutional neural network model based on computer simulation context", Proc. SPIE 12970, Fourth International Conference on Signal Processing and Computer Science (SPCS 2023), 129703A (21 December 2023); https://doi.org/10.1117/12.3012560

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