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Conference Paper
Lightweight Object Recognizer for Edge Devices
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- Authors
- Seungtae Hong, Jeong-Si Kim, Yung Joon Jung
- Issue Date
- 2022-10
- Citation
- International Conference on Information and Communication Technology Convergence (ICTC) 2022, pp.1729-1731
- Publisher
- IEEE
- Language
- English
- Type
- Conference Paper
- Abstract
- Recently, as personal edge devices such as smartphones have become popular, real-time object recognizers are attracting attention. Edge devices not only have limited memory and storage, but also have various types of HW platforms depending on the purpose of use. In particular, depending on the GPU type of edge devices, whether to use a parallel processing framework essential for deep learning computation acceleration such as OpenCL or CUDA is determined. Therefore, for real-time object recognition in various edge devices, it is necessary to support deep learning computation acceleration optimized for the GPU structure of each edge device. To this end, in this paper, we propose a deep learning framework that analyzes the GPU structure of edge devices and automatically applies the optimal parallel processing technique (OpenCL or CUDA) according to the GPU type. In addition, based on the proposed deep learning framework, we implement a real-time object recognizer based on the field image on the edge device.
- KSP Keywords
- Deep learning framework, Edge devices, Field image, Object Recognition, Object recognizer, Real-Time, based on the field, computation acceleration, deep learning(DL), parallel processing technique