An efficient FPGA-Based architecture for convolutional neural networks
| Autor: | Tsung-Ming Tai, Wen-Jyi Hwang, Yun-Jie Jhang |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Hardware architecture
020203 distributed computing Cellular architecture Computer science ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION 02 engineering and technology Facial recognition system Convolutional neural network Software portability ComputingMethodologies_PATTERNRECOGNITION Computer architecture 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Reference architecture Field-programmable gate array Space-based architecture |
| Zdroj: | TSP |
| DOI: | 10.1109/tsp.2017.8076054 |
| Popis: | The goal of this paper is to implement an efficient FPGA-based hardware architectures for the design of fast artificial vision systems. The proposed architecture is capable of performing classification operations of a Convolutional Neural Network (CNN) in realtime. To show the effectiveness of the architecture, some design examples such as hand posture recognition, character recognition, and face recognition are provided. Experimental results show that the proposed architecture is well suited for embedded artificial computer vision systems requiring high portability, high computational speed, and accurate classification. |
| Databáze: | OpenAIRE |
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