| 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 |
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run-time scheduler(Mobicom) |
| 7.FlexDNN: Input-Adaptive On-Device Deep Learning for Efficient Mobile Vision |
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| 8.Boomerang: On-demand cooperative deep neural network inference for edge intelligence on the industrial internet of things |
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| 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 |
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Binary neural network branch |
| 13.A Lightweight Collaborative Recognition System with Binary Convolutional Neural Network for Mobile Web Augmented Reality |
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Binary neural network branch |
| 14.DNN Inference Acceleration with Partitioning and Early Exiting in Edge Computing |
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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 |
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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 |
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(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 |
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Follow up work of Edgent, online inference |
| 20.Branching in Deep Networks for Fast Inference |
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| 21.Accelerating on-device DNN inference during service outage through scheduling early exit |
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| 22.Learning Early Exit for Deep Neural Network Inference on Mobile Devices through Multi-Armed Bandits |
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| 23.Cloudedge-based lightweight temporal convolutional networks for remaining useful life prediction in IIoT |
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two scale prediction |
| 24.Predictive Exit: Prediction of Fine-Grained Early Exits for Computation- and Energy-Efficient Inference |
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(AAAI) |
| 25.DeeCap: Dynamic Early Exiting for Efficient Image Captioning |
Offical Code |
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| 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 |
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| 28.Class-specific early exit design methodology for convolutional neural networks |
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| 29.Federated Learning for Cooperative Inference Systems: The Case of Early Exit Networks |
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Cooperative Inference Systems settings |
| 30.Dual Dynamic Inference: Enabling More Efficient, Adaptive, and Controllable Deep Inference |
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Channel with Early Exit |
| 31.Multi-Exit Semantic Segmentation Networks |
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(ECCV) |
| 32.Multi-Exit DNN Inference Acceleration Based on Multi-Dimensional Optimization for Edge Intelligence |
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a contextual bandit learning that learns the optimal partition point |
| 33.Accelerating on-device DNN inference during service outage through scheduling early exit |
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| 34.Autodidactic Neurosurgeon: Collaborative Deep Inference for Mobile Edge Intelligence via Online Learning |
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| 35.Towards Edge Computing Using Early-Exit Convolutional Neural Networks |
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MobiletNetV2 with early exits |
| 36.Resource-Constrained Edge AI with Early Exit Prediction |
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| 37.Temporal Decisions: Leveraging Temporal Correlation for Efficient Decisions in Early Exit Neural Networks |
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| 38.Efficient Post-Training Augmentation for Adaptive Inference in Heterogeneous and Distributed IoT Environments |
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| 39.DyCE: Dynamic Configurable Exiting for Deep Learning Compression and Scaling |
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| 40.ClassyNet: Class-Aware Early-Exit Neural Networks for Edge Devices |
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| 41.ENASFL: A Federated Neural Architecture Search Scheme for Heterogeneous Deep Models in Distributed Edge Computing Systems |
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| 42.Adaptive Early Exiting for Collaborative Inference over Noisy Wireless Channels |
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| 43.EdgeFM: Leveraging Foundation Model for Open-set Learning on the Edge |
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| 44.Channel-Adaptive Early Exiting using Reinforcement Learning for Multivariate Time Series Classification |
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| 45.SplitEE: Early Exit in Deep Neural Networks with Split Computing |
Offical Code |
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| 46.Branchy Deep Learning Based Real-Time Defect Detection Under Edge-Cloud Fusion Architecture |
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| 47.Joint multi-user DNN partitioning and task offloading in mobile edge computing |
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| 48.Resource-aware Deployment of Dynamic DNNs over Multi-tiered Interconnected Systems |
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| 49.Edge Computing with Early Exiting for Adaptive Inference in Mobile Autonomous Systems |
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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