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This repository is dedicated to self-learning about early exit papers, including relevant code and documentation.

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Early-Exit-Models

This repository is dedicated to self-learning about early exit models, including relevant code and documentation.

Survey Papers code Comments
1. Split Computing and Early Exiting for Deep Learning Applications: Survey and Research Challenges Fundamental Survey Paper Section 4
2. Adaptive Inference through Early-Exit Networks: Design, Challenges and Directions
3. Distributed Artificial Intelligence Empowered by End-Edge-Cloud Computing: A Survey Section III-C-4
4. End-Edge-Cloud Collaborative Computing for Deep Learning: A Comprehensive Survey Section III-B
5.Advancements in Accelerating Deep Neural Network Inference on AIoT Devices: A Survey
6.Resource Management in Mobile Edge Computing: A Comprehensive Survey
Papers code Comments
1. BranchyNet: Fast Inference via Early Exiting from Deep Neural Networks Official Code
code		 Fundamental Paper
2.Distributed Deep Neural Networks over the Cloud, the Edge and End Devices Offical Code Follow up work, node-edge-cloud setting
3.Multi-Scale Dense Networks for Resource Efficient Image Classification Official Code
pytorch		 MSDNet(ICLR)
4.Branchy-GNN: a Device-Edge Co-Inference Framework for Efficient Point Cloud Processing Offical Code GNN
5.EdgeKE: An On-Demand Deep Learning IoT System for Cognitive Big Data on Industrial Edge Devices Offical Code knowledge distillation, early exit to meet latency or accuracy requirements
6.SPINN: Synergistic Progressive Inference of Neural Networks over Device and Cloud run-time scheduler(Mobicom)
7.FlexDNN: Input-Adaptive On-Device Deep Learning for Efficient Mobile Vision
8.Boomerang: On-demand cooperative deep neural network inference for edge intelligence on the industrial internet of things
9.Early-exit deep neural networks for distorted images: providing an efficient edge offloading Offical Code early-exit DNN with expert branches
10.FrameExit: Conditional Early Exiting for Efficient Video Recognition Offical Code gating module (CVPR)
11.BERxiT: Early exiting for BERT with better fine-tuning and extension to regression Offical Code (ACL)
12.A lightweight collaborative deep neural network for the mobile web in edge cloud Binary neural network branch
13.A Lightweight Collaborative Recognition System with Binary Convolutional Neural Network for Mobile Web Augmented Reality Binary neural network branch
14.DNN Inference Acceleration with Partitioning and Early Exiting in Edge Computing
15.Edge Intelligence: On-Demand Deep Learning Model Co-Inference with Device-Edge Synergy (Edgent)
Edge AI: On-Demand Accelerating Deep Neural Network Inference via Edge Computing		 partitions DNN computation between mobile and edge server based on the available bandwidth
16.Improved Techniques for Training Adaptive Deep Networks Offical Code (ICCV)
17.Learning Anytime Predictions in Neural Networks via Adaptive Loss Balancing (AAAI)
18.Learning to Stop While Learning to Predict Offical Code (ICML)
19.DeepAdapter: A Collaborative Deep Learning Framework for the Mobile Web Using Context-Aware Network Pruning Follow up work of Edgent, online inference
20.Branching in Deep Networks for Fast Inference
21.Accelerating on-device DNN inference during service outage through scheduling early exit
22.Learning Early Exit for Deep Neural Network Inference on Mobile Devices through Multi-Armed Bandits
23.Cloudedge-based lightweight temporal convolutional networks for remaining useful life prediction in IIoT two scale prediction
24.Predictive Exit: Prediction of Fine-Grained Early Exits for Computation- and Energy-Efficient Inference (AAAI)
25.DeeCap: Dynamic Early Exiting for Efficient Image Captioning Offical Code
26.A Simple Hash-Based Early Exiting Approach For Language Understanding and Generation Offical Code (ACL)
27.It's always personal: Using Early Exits for Efficient On-Device CNN Personalisation
28.Class-specific early exit design methodology for convolutional neural networks
29.Federated Learning for Cooperative Inference Systems: The Case of Early Exit Networks Cooperative Inference Systems settings
30.Dual Dynamic Inference: Enabling More Efficient, Adaptive, and Controllable Deep Inference Channel with Early Exit
31.Multi-Exit Semantic Segmentation Networks (ECCV)
32.Multi-Exit DNN Inference Acceleration Based on Multi-Dimensional Optimization for Edge Intelligence a contextual bandit learning that learns the optimal partition point
33.Accelerating on-device DNN inference during service outage through scheduling early exit
34.Autodidactic Neurosurgeon: Collaborative Deep Inference for Mobile Edge Intelligence via Online Learning
35.Towards Edge Computing Using Early-Exit Convolutional Neural Networks MobiletNetV2 with early exits
36.Resource-Constrained Edge AI with Early Exit Prediction
37.Temporal Decisions: Leveraging Temporal Correlation for Efficient Decisions in Early Exit Neural Networks
38.Efficient Post-Training Augmentation for Adaptive Inference in Heterogeneous and Distributed IoT Environments
39.DyCE: Dynamic Configurable Exiting for Deep Learning Compression and Scaling
40.ClassyNet: Class-Aware Early-Exit Neural Networks for Edge Devices
41.ENASFL: A Federated Neural Architecture Search Scheme for Heterogeneous Deep Models in Distributed Edge Computing Systems
42.Adaptive Early Exiting for Collaborative Inference over Noisy Wireless Channels
43.EdgeFM: Leveraging Foundation Model for Open-set Learning on the Edge
44.Channel-Adaptive Early Exiting using Reinforcement Learning for Multivariate Time Series Classification
45.SplitEE: Early Exit in Deep Neural Networks with Split Computing Offical Code
46.Branchy Deep Learning Based Real-Time Defect Detection Under Edge-Cloud Fusion Architecture
47.Joint multi-user DNN partitioning and task offloading in mobile edge computing
48.Resource-aware Deployment of Dynamic DNNs over Multi-tiered Interconnected Systems
49.Edge Computing with Early Exiting for Adaptive Inference in Mobile Autonomous Systems

Repos on Early Exit

Papers code Comments
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AI on Edge

Papers code Comments
Enabling AI on Edges: Techniques, Applications and Challenges Offical Code
Green Edge AI: A Contemporary Survey

Early Exit --- Fig. 7 in 1st survey paper Early Exit inference model from Fig.7 in Split Computing and Early Exiting for Deep Learning Applications: Survey and Research Challenges, YOSHITOMO MATSUBARA and MARCO LEVORATO, University of California, Irvine, USA, FRANCESCO RESTUCCIA, Northeastern University, USA

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