人工智慧在現今資訊技術中有著重要的發展地位，藉由各式深度學習平台訓練產生的人工智慧模型，已逐漸使用在現代日常生活的辨識應用中。在人工智慧計算的發展過程中，透過合適的程式語言來開發深度學習應用，並嘗試最佳化底層平行計算的執行，是人工智慧計算應用的關鍵。然而，在高度複雜的深度學習開發環境及其底層平行計算，都需要足夠的專業知識來操作。對具備基礎程式觀念的初學者來說，學習人工智慧領域仍然需要在軟體學習和應用開發上付出很大的時間成本。當前初學者在面對缺乏抽象化設計的人工智慧計算環境，想要短時間掌握專業知識和系統架構會面臨巨大的挑戰。

本論文將為深度學習平台介紹有關Blockly compute和平行計算的兩個抽象化方法來解決上述挑戰。一種是針對深度學習平台的應用層面，提出可建構並堆疊視覺化block的塊設計策略的方法。這些策略為專業領域的複雜程式結構或應用系統的界面架構找到並分解合適的結構特徵來設計出對應的block形狀。而這些設計好的blocks可以藉由Blockly工具來堆疊出特定的應用開發。此外，透過案例研究的討論，展示了深度學習平台TVM和藍牙函式庫的視覺最佳化應用。另一種是針對深度學習平台的系統層面，對支援平行計算的開放計算語言OpenCL kernel提供C++ sparsity抽象化方法。這方法可透過C++ template設計的view layout來抽象化TVM的OpenCL sparse compression kernel內容的表達，並可使OpenCL kernel在語言結構上更加靈活。該方法也可應用結合於深度學習平台的計算函式庫，提供其底層OpenCL kernel支援C++抽象化的實現。此外，還可整合C++ execution policy提供的平行最佳化，為平行計算的複雜度帶來控制和物件的再用性。

總結上述針對深度學習平台的應用和系統兩層面的研究，本論文提出了包含Blockly策略及OpenCL C++ sparsity kernel的抽象化設計。實驗結果顯示，抽象化設計可以為應用結構、特定領域架構帶來視覺最佳化效果，同時也能為系統平行語言kernel結構帶來C++語言特性的最佳化效益。研究結果表明，本論文提出的抽象化設計方法可以作為特定領域函式庫、深度學習平台、及OpenCL kernel的設計參考指南。

Artificial intelligence (AI) is pivotal in technological development currently, and various AI model recognition trained through deep learning platforms has been gradually applied in modern daily life. Throughout the development of AI computing, popularizing deep learning applications through a suitable programming language and optimizing the execution of underlying parallel computing has always been the key to the application of AI computing. However, the highly complex deep learning environment and its underlying parallel computing require sufficient domain knowledge to operate and optimize. To face the field of AI, beginners with an essential programming background must pay a considerable time cost in software learning and application development. The lack of abstraction development design is an urgent challenge for current users to understand domain knowledge and system architecture for AI computing environments.

This thesis presents the abstraction research of Blockly compute and parallel computing to address the above challenge for deep learning platforms in AI computing environments. The research includes two approaches: One is proposing block design strategies to construct and stack visual blocks for the deep learning platform application layer. These strategies find and decompose appropriate features for complex program structures of domain-specific or interface forms of application systems and design corresponding block shapes. These designed blocks can stack block content by using a block-based Blockly tool. In addition, the case study discussion demonstrates the visual optimization of the complex deep learning platform Tensor Virtual Machine (TVM) and domain-specific Bluetooth library. The results shown can significantly reduce the time complexity of learning and development. Another approach is the C++ sparsity abstraction approach to provide a sparsity abstraction design for the Open Computing Language (OpenCL) kernel of the system layer of the deep learning platform. This approach can abstract the feature expression of TVM’s OpenCL sparse compression kernel by using the view layout of the C++ template design and further apply it to the OpenCL kernel of computing libraries. This approach can make the OpenCL kernel code more object-oriented and flexible in language structure. In addition, this approach design can also integrate the parallel optimization of the C++ execution policy, which brings control and object-based reusability to the complexity of parallel computing.

This thesis proposes two abstraction approaches for the application and system layers of the deep learning platform, including Blockly strategies and OpenCL C++ sparsity kernel. After the demonstration and experimental results of the domain-specific library, TVM architecture, and OpenCL kernel structure, these two approaches can bring visual optimization benefits and obtain the C++ language features optimization effect. In other words, the abstraction design approaches proposed in this thesis can serve as a design reference guideline for domain-specific libraries, deep learning platforms, and OpenCL kernels.

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