Merging approach of pre-trained deep learning models for edge-cloud collaborative inference

Wansong Yan

doi:10.1117/12.3051376

18 November 2024 Merging approach of pre-trained deep learning models for edge-cloud collaborative inference

Wansong Yan

Author Affiliations +

Proceedings Volume 13403, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2024) ; 134031N (2024) https://doi.org/10.1117/12.3051376
Event: International Conference on Algorithms, High Performance Computing, and Artificial Intelligence, 2024, Zhengzhou, China

Abstract

Artificial Intelligence (AI)-based Internet of Things (IoT) applications benefit greatly from advanced deep learning models. However, the increasing complexity and resource requirements of deep learning models pose challenges to computational efficiency and deployment on resource-constrained devices. To address these challenges, the paper proposes a merging technique to efficiently merge multiple single-task deep learning models into a unified multi-task model. The proposed method merges models in a serial fashion, potentially replacing various layers with a smaller deep neural sub-network to connect the layers of the previous model and the latter model's layers. This method improves computational efficiency, and can create larger models to perform the functions of the original models with minimal training overhead. The paper designs and implements a merging model that supports end-to-end compressed sensing (CS) sampling and object detection, and the experimental results verify the efficacy of the proposed method in generating fine-tuned multi-task deep neural models with minimal training time and resource costs, making it a cost - efficient solution for edge-cloud collaborative inference.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Wansong Yan "Merging approach of pre-trained deep learning models for edge-cloud collaborative inference", Proc. SPIE 13403, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2024) , 134031N (18 November 2024); https://doi.org/10.1117/12.3051376

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

;

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE