Energy-efficient Edge Approximation for Connected Vehicular Services

Conference paper (2023)

Authors

D. Katare

Aaron Yi Ding

DOI: https://doi.org/10.1109/CISS56502.2023.10089724

Data Compression Energy Efficiency Approximation Edge AI Model compression 3D maps HD map

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Published Date

2023

Language

English

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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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