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研究生: 林坤宏
Kun-Hong Lin
論文名稱: 以非監督式類神經網路實現高維度平行計算之主成分分析的硬體架構實現
Hardware Implementation of Principal Component Analysis for High-Dimensional Parallel Computing by Unsupervised Neural Network
指導教授: 黃文吉
Hwang, Wen-Jyi
學位類別: 碩士
Master
系所名稱: 資訊工程學系
Department of Computer Science and Information Engineering
論文出版年: 2011
畢業學年度: 99
語文別: 中文
論文頁數: 61
中文關鍵詞: 主成分分析可程式化系統晶片
英文關鍵詞: PCA, GHA, SOPC, FPGA
論文種類: 學術論文
相關次數: 點閱:170下載:8
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  • 本論文針對主成分分析(Principle Components Analysis, PCA)提出一個以Generalized Hebbian Algorithm (GHA)為基礎的高維度平行計算之硬體架構。
    我們希望利用硬體的特性來達到平行計算能力,進而加速運算效能,同時希望透過擷取高維度的特徵向量來取得較好的分類成功率,在突觸權重向量更新單元,將原本m筆的資料切割成b等分,重複利用q份硬體電路來運算b次,即m=q×b,m指的是訓練資料的維度,b指的是我們將資料切割成幾等分,q指的是每一等分的資料量,如此一來就可達到硬體共享的機制,也將記憶單元共享給不同的計算元件使用,因此可以降低面積成本(Area Cost),也能實現較高維度的硬體架構。
    我們將硬體電路實作在可程式化系統晶片(System on a Programmable Chip,SOPC)的平台中,並且利用此平台來測試與驗證實驗數據,根據實驗結果來證明我們所提出的硬體架構,是具有較好的分類成功率及較低的硬體資源消耗,也與軟體做時間測量比較,來驗證硬體的加速效能。

    附圖目錄 iv 附表目錄 vii 第一章 緒論 1 第一節 研究背景及動機 1 第二節 研究目的及方法 3 第三節 全文架構 4 第二章 基礎理論介紹 5 第一節 Hebbian-Based Maximum Eigenfilter 5 第二節 Generalized Hebbian Algorithm 8 第三節 SOPC系統整合系統 10 第三章 硬體架構的實現 12 第一節 基礎硬體架構介紹 12 第二節 主成分計算單元 13 第三節 突觸權重更新單元 15 第四節 Architecture A 18 第五節 Architecture B 26 第四章 實驗數據與效能比較 38 第一節 開發平台與實驗環境介紹 38 第二節 實驗數據呈現與討論 42 第五章 結論 58 參考文獻 59

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