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Power Estimation and Energy Efficiency of AI Accelerators on Embedded Systems

Author

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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)

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.

Suggested Citation

  • Minseon Kang & Moonju Park, 2025. "Power Estimation and Energy Efficiency of AI Accelerators on Embedded Systems," Energies, MDPI, vol. 18(14), pages 1-10, July.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:14:p:3840-:d:1705177
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