@inproceedings{4d01afefe83e48209bda614fe3815790,
title = "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.",
keywords = "3D maps, Approximation, Data Compression, Energy Efficiency, Edge AI, HD map, Model compression",
author = "Dewant Katare and Ding, {Aaron Yi}",
note = "Green Open Access added to TU Delft Institutional Repository {\textquoteleft}You share, we take care!{\textquoteright} – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.",
year = "2023",
doi = "10.1109/CISS56502.2023.10089724",
language = "English",
series = "2023 57th Annual Conference on Information Sciences and Systems, CISS 2023",
publisher = "IEEE",
pages = "1--6",
booktitle = "2023 57th Annual Conference on Information Sciences and Systems, CISS 2023",
address = "United States",
}