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    Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/81109


    Title: 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
    Authors: 林榮豪;Lin, Jung-Hao
    Contributors: 資訊工程學系在職專班
    Keywords: 微控制器;嵌入式;神經網路;AIoT;DNN;MCU
    Date: 2019-07-10
    Issue Date: 2019-09-03 15:34:54 (UTC+8)
    Publisher: 國立中央大學
    Abstract: 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。;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.
    Appears in Collections:[資訊工程學系碩士在職專班 ] 博碩士論文

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