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Journal Article
CitiusSynapse: A Deep Learning Framework for Embedded Systems
- Authors
- Seungtae Hong, Hyunwoo Cho, Jeong-Si Kim
- Issue Date
- 2021-12
- Citation
- Applied Sciences, v.11, no.23, pp.1-22
- ISSN
- 2076-3417
- Publisher
- MDPI
- Language
- English
- Type
- Journal Article
- Abstract
- As embedded systems, such as smartphones with limited resources, have become increas-ingly popular, active research has recently been conducted on performing on-device deep learning in such systems. Therefore, in this study, we propose a deep learning framework that is specialized for embedded systems with limited resources, the operation processing structure of which differs from that of standard PCs. The proposed framework supports an OpenCL-based accelerator engine for accelerator deep learning operations in various embedded systems. Moreover, the parallel processing performance of OpenCL is maximized through an OpenCL kernel that is optimized for embedded GPUs, and the structural characteristics of embedded systems, such as unified memory. Furthermore, an on-device optimizer for optimizing the performance in on-device environments, and model con-verters for compatibility with conventional frameworks, are provided. The results of a performance evaluation show that the proposed on-device framework outperformed conventional methods.
- KSP Keywords
- Conventional methods, Deep learning framework, Limited resources, Parallel Processing, Performance evaluation, Structural characteristic, Unified memory, deep learning(DL), device framework, embedded system, processing performance
This work is distributed under the term of Creative Commons License (CCL)
(CC BY)
(CC BY)
