研究生: |
紀凱文 Ji, Kai-Wun |
---|---|
論文名稱: |
摺積神經網路全連結層FPGA實現之研究 The FPGA Implementation of Fully-connected Layers of Convolutional Neural Networks |
指導教授: |
吳榮根
Wu, Jung-Gen 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: | 2016 |
畢業學年度: | 104 |
語文別: | 中文 |
論文頁數: | 62 |
中文關鍵詞: | 類神經網路 、全連結 、摺積神經網路 |
DOI URL: | https://doi.org/10.6345/NTNU202204029 |
論文種類: | 學術論文 |
相關次數: | 點閱:127 下載:24 |
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本論文旨在於FPGA ( Field Programmable Gate Array ) [1] [2]平台設計實現全連結架構,並與摺積神經網路結合,成為高速的人工視覺辨識系統。
本論文之基礎建立於類神經網路之全連結的使用,除了將全連結硬體化之外,並與具有即時運算的能力摺積神經網路(Convolutional Neural Network)整合。現存的摺積神經網路系統大多以GPU實現,雖具有高速的運算,但同時也擁有高功率消耗等缺點。雖然以FPGA為主之設計可有效降低功率消耗,但也有許多可改善之處。首先是在運算過程中會產生許多的中繼結果,這會使記憶體增加儲存資料之負擔;其次是現有硬體實現之架構僅具焦於摺積神經網路內的摺積層架構,往往忽略了其他重要架構像是全連接層(Fully-Connected Layer)之設計,根據上述原因導致無法實現高速及高準確度之人工視覺系統。
本系統採用全連結架構做為硬體實現,此架構大致上可分為2個全連結層,利用將這2個層級硬體化,進而實現高速的全連結運算。除了實現高速運算之外,為了提高此系統的辨識率,將以此架構與摺積神經網路整合,使辨識率大幅增加。此系統通常應用於字元及人臉辨識,透過我們的實驗結果顯示此架構適合使用於需要高速運算、高準確度、高可攜度、低功率消耗等的人工視覺辨識系統之應用程式。
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