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研究生： 許啟宏
Hsu, Chi-Hung
論文名稱： 應用強化學習方法之多目標類神經網路架構探索
MONAS:Multi-Objective Neural Architecture Search using Reinforcement Learning
指導教授： 張世杰
Chang, Shih-Chieh
口試委員: 周志遠
Chou, Chih-Yuan
彭文志
Peng, Wen-Chih
學位類別： 碩士
Master
系所名稱：
論文出版年： 2018
畢業學年度： 106
語文別： 英文
論文頁數： 24
中文關鍵詞： 類神經網路架構探索 、強化學習 、能源效率 、卷積神經網路
外文關鍵詞： Neural Architecture Search, Reinforcement Learning, Energy Efficiency, Convolutional Neural Network
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    • 近年來對類神經架構搜索(Neural Architecture Search ,NAS)的研究表明，自動設計類神經網絡已經能達到與人工設計一樣好的水準。儘管大多數現有的類神經架構搜索都是針對尋找優化預測精度的架構。這些方法可能會產生消耗過高能耗的複雜體系結構，這不適用於能源消耗預算有限的計算環境。本篇論文我們提出MONAS(Multi-Objective Neural Architecture Search)，一種多目標類神經架構搜索，具有新穎的獎勵功能，在探索類神經架構時既考慮預測精度又考慮能源消耗。MONAS 能有效地探索設計空間並探索滿足給定要求的架構。實驗結果表明，MONAS 發現的架構實現了與現有技術模型相當或更好的精確度，同時具有更好的能源效率。

    Recent studies on neural architecture search have shown that automatically designed neural networks perform as good as human-designed architectures. While most existing works on neural architecture search aim at finding architectures that optimize for prediction accuracy. These methods may generate complex architectures consuming excessively high energy consumption, which is not suitable for computing environment with limited power budgets. We propose MONAS, a Multi-Objective Neural Architecture Search with novel reward functions that consider both prediction accuracy and power consumption when exploring neural architectures. MONAS effectively explores the design space and searches for architectures satisfying the given requirements. The experimental results demonstrate that the architectures found by MONAS achieve accuracy comparable to or better than the state-of-the-art models, while having better energy efficiency.

    1 Introduction 1 2 Background 3 3 Methodology 5 3.1 Framework Overview 5 3.2 Implementation Details 6 3.3 Reinforcement Learning Process 7 4 Experiment Setup 11 4.1 Experimental Setup 11 4.2 Power and Energy measurement 12 4.3 Dataset 12 4.4 Image preprocessing 12 4.5 Training Details on AlexNet: 13 4.6 Training Details on CondenseNet: 13 5 Result and Discussion 14 5.1 Adaptability 14 5.2 Efficiency 16 5.3 Pareto Frontier 19 5.4 Discover Better Models 19 6 Conclusion 22 References 23

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