Neural network quantization algorithms based on weights and activation values

Yubo Li; Runbo Wang; Huaping Wu

doi:10.1117/12.2636460

6 May 2022 Neural network quantization algorithms based on weights and activation values

Yubo Li, Runbo Wang, Huaping Wu

Proceedings Volume 12176, International Conference on Algorithms, Microchips and Network Applications; 1217607 (2022) https://doi.org/10.1117/12.2636460
Event: International Conference on Algorithms, Microchips, and Network Applications 2022, 2022, Zhuhai, China

Abstract

In recent years, neural networks have demonstrated attractive performance in a variety of computer vision tasks. This has led to the deployment of CNNs into real-world applications. However, most existing CNNs require a large amount of storage space and computational resources, which hinders their deployment on mobile devices. In order to solve this problem, the existing research shows that quantization and pruning can greatly reduce the memory occupied by the neural network in the device without reducing the accuracy of image recognition, which plays a great role in solving the deployment problem of CNNs. Therefore, this paper classifies and analyzes the quantitative algorithm model of neural network according to the parameters that need to be quantified, summarizes the common defects of the current quantitative algorithm in terms of accuracy, and looks forward to its future development trend.

Citation Download Citation

Yubo Li, Runbo Wang, and Huaping Wu "Neural network quantization algorithms based on weights and activation values", Proc. SPIE 12176, International Conference on Algorithms, Microchips and Network Applications, 1217607 (6 May 2022); https://doi.org/10.1117/12.2636460

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