Quantization and Deployment Study of Classification Models for Embedded Platforms

Zihan Huang, Jin Jin, Chaolong Zhang, Zhijie Xu, Yuanping Xu, Chao Kong, Qin Wen, Dan Tang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


Deep learning models find extensive applications across various domains. However, their large number of parameters, high storage requirements, and computational overhead pose challenges for deploying these models on resource-constrained embedded devices. This study focuses on addressing this issue by exploring techniques to optimize and deploy lightweight models on embedded devices. The approach involves optimization and adjustment of the model, followed by model conversion, quantization, and quantization calibration, aimed at reducing model size and improving inference speed. Notably, improvements are made to the quantization calibration algorithm to mitigate accuracy loss caused by model quantization. The experimental results demonstrate that light quantization significantly reduces model size, facilitating storage on embedded devices. Although there is a slight reduction in accuracy, the inference speed is substantially improved, enabling real-time human face recognition in video scenarios.

Original languageEnglish
Title of host publication2023 28th International Conference on Automation and Computing
Subtitle of host publicationICAC 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9798350335859
ISBN (Print)9798350335866
Publication statusPublished - 16 Oct 2023
Event28th International Conference on Automation and Computing: Digitalisation for Smart Manufacturing and Systems - Aston University, Birmingham, United Kingdom
Duration: 30 Aug 20231 Sep 2023
Conference number: 28


Conference28th International Conference on Automation and Computing
Abbreviated titleICAC 2023
Country/TerritoryUnited Kingdom
Internet address

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