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Journal Article
A Method of Deep Learning Model Optimization for Image Classification on Edge Device
- Authors
- Hyungkeuk Lee, NamKyung Lee, Sungjin Lee
- Issue Date
- 2022-10
- Citation
- Sensors, v.22, no.19, pp.1-15
- ISSN
- 1424-8220
- Publisher
- MDPI
- Language
- English
- Type
- Journal Article
- Project Code
- 22HH5100, Development of intelligent media attribute extraction and sharing technology, Lee Nam Kyung
- Abstract
- Due to the recent increasing utilization of deep learning models on edge devices, the industry demand for Deep Learning Model Optimization (DLMO) is also increasing. This paper derives a usage strategy of DLMO based on the performance evaluation through light convolution, quantization, pruning techniques and knowledge distillation, known to be excellent in reducing memory size and operation delay with a minimal accuracy drop. Through experiments regarding image classification, we derive possible and optimal strategies to apply deep learning into Internet of Things (IoT) or tiny embedded devices. In particular, strategies for DLMO technology most suitable for each on-device Artificial Intelligence (AI) service are proposed in terms of performance factors. In this paper, we suggest a possible solution of the most rational algorithm under very limited resource environments by utilizing mature deep learning methodologies.
- KSP Keywords
- AND operation, Edge devices, Embedded Devices, Image classification, Internet of thing(IoT), Memory size, Model optimization, Performance evaluation, Performance factors, Pruning techniques, artificial intelligence
This work is distributed under the term of Creative Commons License (CCL)
(CC BY)
(CC BY)
