IEEE CISS 2023

Energy-efficient Edge Approximation for Connected Vehicular Services

Abstract:

Connected vehicular services depend heavily on communication as they 
frequently transmit data and AI models/weights within the vehicular 
ecosystem. Energy efficiency in vehicles is crucial to keep up with 
the fast-growing demand for vehicular data processing and communication. 
To tackle this rising challenge, we explore approximation and edge AI 
techniques for achieving energy efficiency for vehicular services. 
Focusing on data-intensive vehicular services, we present an experimental 
case study on the high-definition (HD) map using the model partition 
approach. Our study compares the AI model energy consumption using 
multiple approximation ratios over embedded edge devices. Based on 
experimental insights, we further discuss an envisioned approximate 
Edge AI pipeline for developing and deploying energy-efficient 
vehicular services.


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IEEE Library

@inproceedings{Katare:CISS2023,
 author = {Katare, Dewant and Ding, Aaron Yi},
 title = {Energy-efficient Edge Approximation for Connected Vehicular Services},
 booktitle = {Proceedings of the 57th Annual Conference on Information Science and Systems},
 series = {CISS '23},
 year = {2023},
 publisher = {IEEE}
}

Dewant Katare, Aaron Yi Ding. 2023. "Energy-efficient Edge Approximation for Connected Vehicular Services". In Proceedings of the 57th Annual Conference on Information Science and Systems (CISS '23).