An efficient FPGA-based architecture for convolutional neural networks

Wen-Jyi Hwang, Yun Jie Jhang, Tsung Ming Tai

研究成果: 書貢獻/報告類型會議論文篇章

2 引文 斯高帕斯(Scopus)

摘要

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.

原文英語
主出版物標題2017 40th International Conference on Telecommunications and Signal Processing, TSP 2017
編輯Norbert Herencsar
發行者Institute of Electrical and Electronics Engineers Inc.
頁面582-588
頁數7
ISBN(電子)9781509039821
DOIs
出版狀態已發佈 - 2017 十月 19
事件40th International Conference on Telecommunications and Signal Processing, TSP 2017 - Barcelona, 西班牙
持續時間: 2017 七月 52017 七月 7

出版系列

名字2017 40th International Conference on Telecommunications and Signal Processing, TSP 2017
2017-January

其他

其他40th International Conference on Telecommunications and Signal Processing, TSP 2017
國家/地區西班牙
城市Barcelona
期間2017/07/052017/07/07

ASJC Scopus subject areas

  • 電腦網路與通信
  • 訊號處理

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