基於CNN之多層感知分類器的在線測試與修復之系統化設計與分析

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研究生：	陳琦 Chen, Chi
論文名稱：	基於CNN之多層感知分類器的在線測試與修復之系統化設計與分析 Systematic Design and Evaluation for Online Test and Repair of CNN-Based MLP Classifiers
指導教授：	吳誠文 Wu, Cheng-Wen
口試委員:	黃婷婷 Hwang, Ting-Ting 黃稚存 Huang, Chih-Tsun 溫瓌岸 Wen, Kuei-Ann
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：	英文
論文頁數：	55
中文關鍵詞：	自我測試、自我修復、深度學習、可靠度、錯誤容忍度
外文關鍵詞：	Self-testing, Self-repair, Deep Learning, Reliability, Fault Tolerance
相關次數：	點閱：2 下載：0
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本論文針對深度學習網路提出了一個全新的在線測試與修復架構,並提出了一個相對應的系統化設計與分析方法。在實驗中,我們用了 10 種不同的深度網路模型,包括 VGG11/16/19、ResNet18/34/50、以及 DenseNet121/161/169 來模擬和驗證我們提出的架構和方法。實驗結果顯示,我們所提出的架構和方法能夠在不同的神經網路錯誤分佈下,有效地提升因錯誤而下降的模型準確度。同時,我們也發現在有錯誤的情況下,基於 ReLU 的模型比基於 SELU 的模型有較高的錯誤容忍度。另外,基於 ReLU 的 DenseNet169 分類器在錯誤尺寸為 8 且錯誤數量多達 512 時,其準確度下降率只有-1.78%。基於本論文所提出的架構及方法,我們能夠有效率地在不同的錯誤分佈下,找出針對特定深度網路模型的高可靠性在線測試與修復架構。

In this thesis, we present a novel systematic evaluation flow for online test and repair of CNN-based MLP classifiers. Also, we propose an enhanced scheme for the classifiers, which are able to achieve smaller accuracy drops in different error distributions. To demonstrate the proposed evaluation flow and the enhanced scheme, we experimented with 10 different CNN-based classifiers, including VGG11/13/16/19, ResNet18/34/50, and DenseNet121/161/169, whose numbers of layers range from 11 to 169. The experimental results show that the enhanced ReLU-based VGG13 classifier can achieve only -2.94% accuracy drop when error size is 8 and error counts in the last hidden layer are up to 2,048. In addition, based on the proposed systematic flow, we found that the ReLU-based DenseNet169 classifier can achieve only -1.78% accuracy drop natively when error size is 8 and error counts are up to 512.

Abstract i
Contents ii
List of Figures..............................................................................................................iv
List of Tables................................................................................................................vi
List of Equations........................................................................................................vii
Chapter 1 Introduction..............................................................................................1
Chapter 2 Fundamentals of the Artificial Neural Network ...................................5
2.1 Neurons, Activation Functions, and MLP...........................................................5
2.2 Convolution Operation and Pooling....................................................................8
Chapter 3 MLP Error Definition............................................................................ 11
Chapter 4 The Proposed Online Test and Repair Scheme ...................................15
4.1 Additional Layers for Online Test and Repair ..................................................15
4.2 Training and Testing of The Proposed Scheme.................................................19
Chapter 5 The Proposed Systematic Evaluation Flow .........................................20
Chapter 6 Experimental Results.............................................................................23
6.1 Experiment Setup ..............................................................................................23
6.2 Experiments of VGG11/13/16/19 .....................................................................24
6.2.1 Output Value Distributions of VGG-based Classifiers................................24
6.2.2 Accuracy Evaluation of Original and Enhanced VGG-based Classifiers....25
6.3 Experiments of ResNet 18/34/50 ......................................................................33
6.3.1 Output Value Distributions of ResNet-based Classifiers.............................33
6.3.2 Accuracy Evaluation of Original and Enhanced ResNet-based Classifiers 34
6.4 Experiments of DenseNet 121/161/169 ............................................................41
6.4.1 Output Value Distributions of DesNet-based Classifiers ............................41
6.4.2 Accuracy Evaluation of Original and Enhanced DesNet-based Classifiers 42

iii

6.5 Overhead Analysis ............................................................................................48
Chapter 7 Conclusions and Future Work..............................................................50
7.1 Conclusions.......................................................................................................50
7.2 Future Work.......................................................................................................50
Bibliography ...............................................................................................................52
                                

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