Leveraging Temporal Patterns: Automated Augmentation to Create Temporal Early Exit Networks for Efficient Edge AI

Leveraging Temporal Patterns: Automated Augmentation to Create Temporal Early Exit Networks for Efficient Edge AI

In embedded systems, efficient deep learning solutions are crucial to balance accuracy and resource constraints. Early Exit Neural Networks offer a promising solution, but their manual configuration and optimization hinder widespread adoption. To overcome this hurdle, we propose a novel, fully autom...

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Bibliographic Details
Main Authors: Max Sponner, Lorenzo Servadei, Bernd Waschneck, Robert Wille, Akash Kumar
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Access
Subjects:
Embedded deep learning
embedded systems
early exit neural networks
conditional deep learning
edge AI
Online Access:https://ieeexplore.ieee.org/document/10752535/
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https://ieeexplore.ieee.org/document/10752535/

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