1 INTRODUCTION

2 OVERVIEW OF LOCAL, EDGE, SPLIT COMPUTING AND EARLY-EXIT MODELS  
2.1 Local and Edge Computing  
2.2 Split Computing and Early Exiting

3 BACKGROUND OF DEEP LEARNING FOR MOBILE APPLICATIONS  
3.1 Lightweight Models  
3.2 Model Compression

4 SPLIT COMPUTING: A SURVEY  
4.1 Split Computing without DNN Modification  
4.2 The Need for Bottleneck Injection  
4.3 Split Computing with Bottleneck Injection  
4.4 SC with Bottlenecks: Training Methodologies

5 EARLY EXITING: A SURVEY  
5.1 Rationale behind EE  
5.2 EE for CV Applications  
5.3 EE for NLP Applications  
5.4 Training Methodologies for EE Strategies

6 SPLIT COMPUTING AND EARLY EXITING: RESEARCH CHALLENGES

7 CONCLUSIONS