研究生: |
田敬瑄 Tien, Ching-Hsuan |
---|---|
論文名稱: |
利用硬體加速器在RISC-V平台實現智慧手勢識別之研究 Research on Implementing Smart Gesture Recognition Using Hardware Accelerators on the RISC-V Platform |
指導教授: |
黃文吉
Hwang, Wen-Jyi |
口試委員: |
黃文吉
Hwang, Wen-Jyi 葉佐任 Yeh, Tso-Zen 林群富 Lin, Chun-Fu |
口試日期: | 2024/07/30 |
學位類別: |
碩士 Master |
系所名稱: |
資訊工程學系 Department of Computer Science and Information Engineering |
論文出版年: | 2024 |
畢業學年度: | 112 |
語文別: | 中文 |
論文頁數: | 49 |
中文關鍵詞: | 手勢辨識 、邊緣運算 、硬體加速器 、神經網路模型 |
研究方法: | 實驗設計法 |
DOI URL: | http://doi.org/10.6345/NTNU202401782 |
論文種類: | 學術論文 |
相關次數: | 點閱:85 下載:0 |
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隨著手勢辨識技術在多媒體娛樂和智慧家電控制等領域的廣泛應用,隱私保護和低延遲推論速度已成為提升用戶體驗的關鍵因素。邊緣計算,由於其能在本地設備上即時處理數據,強化了數據的隱私保護並顯著減少數據傳輸和處理的延時,因而被重視。
本研究開發的智慧手套手勢辨識系統採用開源的RISC-V指令集架構SoC,並在FPGA平台上實現了低成本及高效能的部署。透過整合Gemmini硬體加速器,本系統顯著提升了邊緣設備的計算效能及模型的推論速度。
實驗結果顯示,配備硬體加速器的SoC相較於未搭載加速器的SoC,推論速度提升達55倍,同時維持了手勢識別的高準確度。該邊緣系統的實施不僅確保了用戶數據的安全,也通過硬體加速器顯著降低了推論時間,進一步提升了用戶體驗。本研究證明了開源技術和硬體加速器在邊緣計算領域的有效性,為未來智慧裝置的技術進步提供了一個經濟且高效的解決方案。
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