Power Estimation and Energy Efficiency of AI Accelerators on Embedded Systems

Minseon Kang and Moonju Park ()
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Minseon Kang: Department of Computer Science & Engineering, Incheon National University, Incheon 22012, Republic of Korea
Moonju Park: Department of Computer Science & Engineering, Incheon National University, Incheon 22012, Republic of Korea

Energies, 2025, vol. 18, issue 14, 1-10

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.

Keywords: embedded system; power consumption; energy efficiency; AI accelerator (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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