博碩士論文 106552025 詳細資訊




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姓名 林榮豪(Jung-Hao Lin)  查詢紙本館藏   畢業系所 資訊工程學系在職專班
論文名稱 基於微控制器的嵌入式深度神經網路系統開發及快速應用佈署
(Microcontroller-based Embedded Deep Neural Network System Development and Rapid Application Deployment)
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檔案 [Endnote RIS 格式]    [Bibtex 格式]    [相關文章]   [文章引用]   [完整記錄]   [館藏目錄]   至系統瀏覽論文 (2024-6-17以後開放)
摘要(中) 目前主流的 AI 技術多半依賴高性能之電腦主機,而運行於微控制器上之嵌 入式系統並不多見。若是能將已趨成熟之 AI 技術運行於微控制器上,不僅可以 讓 AI 技術在更多領域應用,還可在許多應用中帶來可觀的功耗的節約。本研究 藉由加入了虛擬機器來使微控制器獲得靈活佈署的特性,從而使實作於微控制 器之深度神經網路應用可用更方便的方式佈署。在彌補了微控制器與電腦主機 應用深度神經網路在便利性上的差距後,前述之目標將變得切實可行。接著進 行的實驗中也驗證了虛擬機器的功能以及實作神經網路的正確性。本研究在將 深度神經網路應用帶進微控制的嵌入式系統中的同時,保留了微控制器的優 點,並克服了它的缺點。與高耗能高成本主機相比,本研究極低的功率消耗將 帶來可觀的節能效益。
摘要(英) At present, mainstream AI technology mostly relies on high-performance computer hosts, and embedded systems running on microcontrollers are rare. If the mature AI technology can be run on the microcontroller, you can not only let AI technology be applied in more fields, but also bring considerable power saving in many applications. In this research, the virtual machine was added to enable the microcontroller to be flexibly deployed, so that the deep neural network application implemented in the microcontroller can be deployed in a more convenient manner. After making up for the gap in the convenience of the deep neural network between the microcontroller and the host computer, the aforementioned goals will become feasible. The subsequent experiments also verified the function of the virtual machine and the correctness of the implemented neural network. This research preserves the advantages of the microcontroller and overcomes its shortcomings while bringing deep neural network applications into the microcontroller-based embedded system. Compared with high-energy and high-cost hosts, the extremely low power consumption system of this research will bring considerable energy savings.
關鍵字(中) ★ 微控制器
★  嵌入式
★  神經網路
關鍵字(英) ★ AIoT
★  DNN
★  MCU
論文目次 摘要 i
Abstract ii
誌謝 iii
目錄 iv
圖目錄 vi
表目錄 vii
第一章 緒論 1
1.1研究背景 1
1.2研究目的 2
1.3論文架構 2
第二章 技術回顧 3
2.1 IEC61131、 PLCopen與Beremiz 3
2.2 機器視覺 4
2.3 深度神經網路 4
2.4 MIAT方法論 5
2.5 GVM 7
第三章 神經網路模組化設計與新一代之GVM 9
3.1 深度神經網路 9
3.1.1 執行期間的資料結構 9
3.1.2 傳輸階段之拓撲編碼 11
3.2 GVM之改良 13
3.2.1擴展參數表 13
3.2.2主流程修改 14
第四章 系統實作與驗證 18
4.1平台佈署概觀 18
4.2 GVM功能函式 19
4.3系統驗證 20
4.3.1 於PVC使用GVM之影像處理實作及系統驗證 20
4.3.1.1 SFC任務設計 20
4.3.1.2 解析XML為參數表 20
4.3.1.3 傳送參數表 21
4.3.1.4 驗證運行結果 22
4.3.2 DNN驗證 23
4.3.2.1 資料集以及網路結構 23
4.3.2.2 架構選定及參數調整 23
4.3.2.3 訓練環境、應用環境及測試結果 24
4.3.2.4 時間資源及空間資源分析 27
4.3.3整合驗證 28
4.3.3.1 SFC設計 29
4.3.3.2 軟體UART與4位七段顯示器外部設備 29
4.3.3.3 實驗環境 31
4.4系統比較 31
4.4.1 伺服主機和工業電腦 32
4.4.2 單一任務之嵌入式系統 32
第五章 結論與未來方向 33
5.1結論 33
5.2未來方向 33
參考文獻 35
參考文獻 參考文獻
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指導教授 陳慶瀚(Ching-Han Chen) 審核日期 2019-7-10
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