Artificial Intelligence Design on Embedded Board with Edge Computing for Vehicle Applications
| Autor: | Wen-Cheng Lai, Ting-Jia Guo, Hui-Chiao Chen, Ching-Lung Su, Yu-Kai Zhang, Yi-Jiun Hung |
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| Rok vydání: | 2020 |
| Předmět: |
0209 industrial biotechnology
Artificial neural network Computer science business.industry 02 engineering and technology Column (database) Image (mathematics) 020901 industrial engineering & automation 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Quantization (image processing) business Edge computing Computer hardware |
| Zdroj: | AIKE |
| Popis: | This article proposes advanced driver assistance system (ADAS) from neural network by YOLO v3-tiny on vehicle platform of NXP S32V234 with edge computing to detect pedestrians and knights. The implemented embedded board has limitation to perform a lot of convolution. As proposed design need to reduce the amount of operation, the considered problem of reduced precision at the same time. The proposed architecture uses method of Squeeze Net and quantization to reduce the amount of operation about 46% and the precision has only slightly reduced. The proposed methods of image to column (Im2col) and memory efficient convolution (MEC) rearranges continuous matrix space to access. The proposed hardware of APEX uses to accelerate operations can reduce execution time and increase detection speed by ten multiples compared with YOLO v3-tiny architecture. |
| Databáze: | OpenAIRE |
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