Evolutionary Search Algorithm for the Optimized Dataflow Engine in Deep Learning
- Title
- Evolutionary Search Algorithm for the Optimized Dataflow Engine in Deep Learning
- Alternative Title
- 심층신경망 연산에 최적화된 NPU 구조를 위한 진화 알고리즘
- Author(s)
- Jooyeon Lee
- DGIST Authors
- Jooyeon Lee ; Jaeha Kung ; Sungjin Lee
- Advisor
- 궁재하
- Co-Advisor(s)
- Sungjin Lee
- Issued Date
- 2021
- Awarded Date
- 2021/02
- Type
- Thesis
- Table Of Contents
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Ⅰ. Introduction 1
Ⅱ. Background 1
2.1 Multilayer Neural Networks 1
2.2 Convolution Neural Network (CNN) 3
2.3 Detailed Description of Each Layer in Convolution Neural Network 3
2.3.1 Convolution Layer 3
2.3.2 Depthwise Separable Convolution Layer 4
2.3.3 Non-Linearity 5
2.3.3 Pooling Layer 5
Ⅲ. Prior work: Dataflow Simulation Engine 6
3.1 Definition of Dataflow 6
3.2 NPU Dataflow Simulators 6
3.3 Nested Loops in Computing DNN Layers 8
3.4 Semantic Representation 9
3.5 Examples of Previous Dataflow Models 11
3.5.1 Weight Stationary Dataflow 11
3.5.2 Row Stationary Dataflow 12
3.6 Open-source Dataflow Simulator 13
Ⅳ. Finding Optimized Dataflow with Evolutionary Algorithm 13
4.1 Evolutionary Algorithm for Searching the Optimized Dataflow 13
4.2 Details on Mutation Operators 15
Ⅴ. Experimental Results 16
Ⅵ. Conclusion 19
- URI
-
http://dgist.dcollection.net/common/orgView/200000366431
http://hdl.handle.net/20.500.11750/16728
- Degree
- Master
- Department
- Information and Communication Engineering
- Publisher
- DGIST
- Related Researcher
-
-
Kung, Jaeha
- Research Interests 딥러닝; 가속하드웨어; 저전력 하드웨어; 고성능 시스템
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- Files in This Item:
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- Appears in Collections:
- Department of Electrical Engineering and Computer Science Theses Master
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