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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 廖世偉 | |
| dc.contributor.author | Bo-Ru Zhao | en |
| dc.contributor.author | 趙柏儒 | zh_TW |
| dc.date.accessioned | 2021-06-17T09:08:22Z | - |
| dc.date.available | 2024-12-02 | |
| dc.date.copyright | 2019-12-02 | |
| dc.date.issued | 2019 | |
| dc.date.submitted | 2019-11-14 | |
| dc.identifier.citation | [1] Khronos Group, 'The OpenVX API for hardware acceleration', https: //www.khronos.org/openvx, 2013.
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/74828 | - |
| dc.description.abstract | 在本研究中，我們研究如何使用特定領域程式語言 – Halide來建構框架以便快速設計和優化OpenVX連通圖。Halide是一款高階影像處理語言，其提供開發者在寫程式時將演算法和排程分離，使開發環境變得更友善，Halide也已被證明是一種用於寫高效能影像處理程式的有效系統。我們利用OpenVX和Halide建構框架以執行影像處理，因為Halide具備OpenVX所缺乏的原語排程，故我們使用Halide來執行OpenVX kernels，此方法使開發者能增加更多開發性並達到更好的效能。我們使用五個實驗來測試，所得到的結果顯示使用Halide搭配OpenVX能顯著提升影像處理及卷積神經網路的效能。 | zh_TW |
| dc.description.abstract | In this study, we investigate how to use a Domain-Specific Language – Halide to build a framework for fast prototyping and optimization of OpenVX graphs. Halide is a new high-level image processing pipeline language. It offers developers to separate the program into algorithms and schedule. This makes developers program friendly. The Halide image processing language has also proven to be an effective system for authoring high-performance image processing code. We built a framework with OpenVX and Halide to implement the image processing system. Since OpenVX is a lack of scheduling primitives, but Halide does. We implemented Halide into OpenVX graphs. This method can significantly improve the performance of image processing and convolutional neural networks. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-17T09:08:22Z (GMT). No. of bitstreams: 1 ntu-108-R06922013-1.pdf: 7505472 bytes, checksum: 7594f1e5fc7963b4c21f0c78a884da89 (MD5) Previous issue date: 2019 | en |
| dc.description.tableofcontents | 口試委員會審定書 #
致謝 i 摘要 ii Abstract iii Contents iv 圖目錄 vi 表目錄 vii Chapter 1 緒論 1 1.1 研究背景 1 1.2 研究動機 3 1.3 研究方法 4 Chapter 2 研究背景及文獻探討 5 2.1 OpenVX 5 2.2 Halide 11 2.2.1 生產者-消費者局部性排程 14 2.2.2 輸入資料重用排程 15 2.3 研究背景 16 Chapter 3 實驗設計及優化方法 18 3.1 排程優化問題 18 3.2 排程演算法 19 3.2.1 函式預處理 20 3.2.2 函式分組和平鋪 21 3.2.3 函式內嵌 25 3.2.4 最終函式產生 26 3.3 實驗設計 27 Chapter 4 實驗結果 28 4.1 OpenVX與Halide資料存取模式比較 28 4.2 OpenVX 上單一kernel替換成Halide 30 4.3 OpenVX 上使用Halide實現Kernels Merge 32 4.4 OpenVX 上使用Halide對連通圖做優化 33 4.5 OpenVX NNE 上使用Halide做優化 34 Chapter 5 結論 37 參考文獻 38 | |
| dc.language.iso | zh-TW | |
| dc.subject | Halide | zh_TW |
| dc.subject | 影像處理 | zh_TW |
| dc.subject | 卷積神經網路 | zh_TW |
| dc.subject | OpenVX | zh_TW |
| dc.subject | OpenVX | en |
| dc.subject | Halide | en |
| dc.subject | image processing | en |
| dc.subject | convolutional neural networks | en |
| dc.title | 使用Halide框架設計和優化OpenVX應用 | zh_TW |
| dc.title | Design and Optimization of OpenVX Applications with Halide framework | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 108-1 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 洪士灝,游逸平,洪明郁,葉羅堯 | |
| dc.subject.keyword | OpenVX,Halide,影像處理,卷積神經網路, | zh_TW |
| dc.subject.keyword | OpenVX,Halide,image processing,convolutional neural networks, | en |
| dc.relation.page | 41 | |
| dc.identifier.doi | 10.6342/NTU201904278 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2019-11-14 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 資訊工程學研究所 | zh_TW |
| 顯示於系所單位： | 資訊工程學系 | |
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| ntu-108-1.pdf 未授權公開取用 | 7.33 MB | Adobe PDF |
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