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研究生: 鍾台湘
Zhong, Tai-Siang
論文名稱: 可應用於心律不整分類之人工智慧硬體加速器設計與實現
FPGA Design and Implementation of A CNN Accelerator for Arrhythmia Classification
指導教授: 李順裕
Lee, Shuenn-Yuh
共同指導教授: 陳儒逸
Chen, Ju-Yi
學位類別: 碩士
Master
系所名稱: 電機資訊學院 - 電機工程學系
Department of Electrical Engineering
論文出版年: 2022
畢業學年度: 111
語文別: 中文
論文頁數: 99
中文關鍵詞: 心電圖生理訊號處理心律不整分類人工智慧卷積神經網路量化剪枝硬體加速器
外文關鍵詞: Biomedical system, ECG, CNN, Edge computing, Artificial Intelligence Accelerator, Electrocardiography, Tensor Tensor Multiply, Inner Product Matrix Vector Multiply, Data reuse, Field Programmable Gate Array, NCKU CBIC ECG Database, MIT-BIH Arrhythmia Database, Edge friendly
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  • 摘要 I 致謝 VII 目錄 VIII 表目錄 X 圖目錄 XII 第1章 緒論 1 1.1 研究動機 1 1.2 章節結構 4 第2章 心律不整與人工智慧簡介 5 2.1 心電圖介紹 5 2.2 心律不整介紹 8 2.3 心律不整資料庫介紹 9 2.3.1 MIT-BIH Arrhythmia Database 9 2.3.2 NCKU CBIC ECG Database 11 2.4 機器學習 21 2.5 類神經網路 23 2.6 深度學習 27 2.7 卷積神經網路 27 2.7.1 卷積層 29 2.7.2 池化層 30 2.7.3 全連接層 32 第3章 軟體模擬與設計 35 3.1 輕量化模型建立 36 3.2 軟體硬體協同設計與硬體性能分析優化 44 3.2.1 剪枝 46 第4章 硬體架構設計與優化 50 4.1 卷積層行為分析 50 4.2 全連接層行為分析 54 4.3 有效率的資料再使用和PSPEAA 56 4.4 AIA架構 75 4.5 現場可程式化邏輯閘陣列 82 第5章 實驗結果與比較 84 5.1 軟體結果 84 5.2 硬體結果 85 5.3 最佳化結果 89 5.4 作品比較 90 第6章 結論與未來展望 93 6.1 結論 93 6.2 未來展望 93 參考文獻 95

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