Year: 2025
Type: article
Abstract: The rapid expansion of IoT devices poses new challenges for AI-driven services, particularly in terms of energy consumption. Although cloud-based AI processing has been the dominant approach, its high energy consumption calls for more energy-efficient alternatives. Edge computing offers an approach for reducing both latency and energy consumption. In this paper, we propose a methodology for estimating the power consumption of AI accelerators on an embedded edge device. Through experimental evaluations involving GPU- and Edge TPU-based platforms, the proposed method demonstrated estimation errors below 8%. The estimation errors were partly due to unaccounted power consumption from main memory and storage access. The proposed approach provides a foundation for more reliable energy management in AI-powered edge computing systems.
Source: Energies
DOI: 10.3390/en18143840
Authors: Minseon Kang, Moonju Park
Institution: Incheon National University
Language: English
Subfield: Hardware and Architecture
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,137
Funder: Incheon National University
OpenAlex ID: W4412513870