1. Introduction

2. Convolutional Neural Network  
2.1. Definitions  
2.2. Training and Inference  
2.3. Convolution Operations  
2.4. Efficient Structure  
2.5. Batch Normalization  
2.6. Pooling  
2.7. Parameters  

3. Pruning  
3.1. Static Pruning  
3.2. Dynamic Pruning  
3.3. Comparisons  

4. Quantization  
4.1. Quantization Algebra  
4.2. Quantization Methodology  
4.3. Quantization Deployment  
4.4. Quantization Reduces Over-fitting  

5. Summary  
5.1. Pruning  
5.2. Quantization  

6. Future Work  

7. Conclusions