Chapter 1
Introduction
1.1 Deep Learning  
1.2 Green Deep Learning  
1.3 Outline of the Survey  

Chapter 2  
Compact Architecture  
2.1 Component Design  
2.2 Component Assembling  
2.3 Compact-architecture Search  

Chapter 3  
Energy-Efficient Training  
3.1 Initialization  
3.2 Normalization  
3.3 Progressive Training  
3.4 Efficient Hyper-parameter Optimization  

Chapter 4  
Energy-Efficient Inference  
4.1 Model Pruning  
4.2 Low-rank Factorization  
4.3 Quantization  
4.4 Knowledge Distillation  

Chapter 5  
Efficient Data Usage  
5.1 Active Learning  
5.2 Pre-training as Few-shot Learners  

Chapter 6  
Conclusions and Future Directions