近年來人工智慧(Artificial Intelligence)的興起，許多應用(例如:人臉辨識、物件偵測等)靠著卷積神經網路(Convolutional Neural Network, CNN)能有更好的正確率。雖然CNN在影像辨識上有不錯的成果，但其需大量的運算一直是反應時間的瓶頸，特別是佈署在運算能力較弱的邊端裝置上(例如:手機、IoT裝置)。許多研究透過霧運算(Fog Computing)及行動邊緣運算(Mobile Edge Computing)來減低其反應時間，基於此環境下發展出的切割深度神經網路模型/資料(Deep Neural NetworkModel/Data Partition)的分散式佈署技術。
然而，不嚴謹的模型/資料切割架構會使得反應時間不減反增，如何選取最佳邊端伺服器(Edge Server)的數量以協助運算及如何做適當的資料切割與佈署神經網路模型是一大挑戰。
在本論文中，我們探討了一個最佳化問題，名為CONVENE目標是最小化卷積神經網路的反應時間。為了理解該問題的基本特性，我們首先將CONVENE簡化成CONVENE-SC，在只有一個通道傳輸資料的情況下設計了一個演算法，名為THREAD-SC。接著，進一步為了解決CONVENE，我們延伸THREAD-SC的概念提出另一個演算法，名為THREAD，此演算法考量了多個通道傳輸資料情況下邊端伺服器數量的選擇與適當的模型與資料分配，以達成更短的反應時間。模擬結果顯示，我們的演算法(THREAD)相較於其他研究方法減少大約50％的時間。實際的反應時間測量也約與模擬情況相符。

Inference acceleration has drawn much attention to cope with the real-time requirement of artificial intelligence (AI) applications. Normally, the overall AI model is offloaded to the cloud for acceleration, which may cause a heavy workload for the backhaul networks and clouds. In order to alleviate the workload, model partition for Convolutional Neural Networks (CNN) has been proposed to utilize the parallel and distributed computing units (e.g., mobile edge servers). The previous works concentrated on the load balancing among servers, however, it may omit the interplay between the computing and communication. It makes the existing approaches less efficient especially in mobile edge networks at which smart devices usually equipped with limited computing capacity that leads to the necessity of offloading the tasks via limited bandwidth capacity to nearby servers. On the other hand, smart devices may have one or multiple available channels which can be used to accelerate transmission in parallel. Therefore, in this thesis, we propose a new system and formulate a new optimization problem, Cooperative CNN Deployment over Mobile Network Edges (CONVENE), to minimize the completion time of inference for the smart devices through one or more available channels. To explore the intrinsic properties, we first study CONVENE with Single Channel and derive an algorithm termed Time-Length-Aware Cooperator Selection and Model Partition Algorithm with Single Channel (THREAD-SC) based on cumulative servers laws addressing suitable number of servers and elastic model partition to get the optimum solution. Next, an extension, Time-Length-Aware Cooperator Selection and Model Partition Algorithm (THREAD), is proposed to subtly utilize multiple channels to further reduce completion time. The simulation and implementation results manifest that our system DeepCo with our algorithm THREAD reduces the total completion time by 50% compared with other naive solutions and works effectively in practice.

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