Detailed info

Improving the Accuracy of Early Exits in Multi-Exit Architectures via Curriculum Learning

Authors

Arian Bakhtiarnia, Qi Zhang, Alexandros Iosifidis

Title

Improving the Accuracy of Early Exits in Multi-Exit Architectures via Curriculum Learning

Abstract

Deploying deep learning services for time-sensitive and resource-constrained settings such as IoT using edge computing systems is a challenging task that requires dynamic adjustment of inference time. Multi-exit architectures allow deep neural networks to terminate their execution early in order to adhere to tight deadlines at the cost of accuracy. To mitigate this cost, in this paper we introduce a novel method called Multi-Exit Curriculum Learning that utilizes curriculum learning, a training strategy for neural networks that imitates human learning by sorting the training samples based on their difficulty and gradually introducing them to the network. Experiments on CIFAR-10 and CIFAR-100 datasets and various configurations of multi-exit architectures show that our method consistently improves the accuracy of early exits compared to the standard training approach.

ISBN

978-1-6654-4597-9

Conference

International Joint Conference on Neural Networks 2021

Date

19/07/2021

Location

Shenzhen, China and Virtually

Year of Publication:

2021

Urlhttps://zenodo.org/record/5517645#.YUr8vH1RVPY
DOIhttps://doi.org/10.1109/IJCNN52387.2021.9533875
  
  

Key Facts

  • Project Coordinator: Dr. Sotiris Ioannidis
  • Institution: Foundation for Research and Technology Hellas (FORTH)
  • E-mail: marvel-info@marvel-project.eu 
  • Start: 01.01.2021
  • Duration: 36 months
  • Participating Organisations: 17
  • Number of countries: 12

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Funding

eu FLAG

This project has received funding from the European Union’s Horizon 2020 Research and Innovation program under grant agreement No 957337. The website reflects only the view of the author(s) and the Commission is not responsible for any use that may be made of the information it contains.