Toward Improving Ensemble-Based Collaborative Inference at the Edge

Toward Improving Ensemble-Based Collaborative Inference at the Edge

Ensemble-based collaborative inference systems, Edge Ensembles, are deep learning edge inference systems that enhance accuracy by aggregating predictions from models deployed on each device. They offer several advantages, including scalability based on task complexity and decentralized functionality...

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Bibliographic Details
Main Authors: Shungo Kumazawa, Jaehoon Yu, Kazushi Kawamura, Thiem Van Chu, Masato Motomura
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Access
Subjects:
Ensemble
edge computing
collaborative inference
neural networks
cascade
test time augmentation
Online Access:https://ieeexplore.ieee.org/document/10384371/

Internet

https://ieeexplore.ieee.org/document/10384371/

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