Power Management in Embedded AI Systems: A Multi-Layered Approach for Edge Computing Applications
DOI:
https://doi.org/10.5281/zenodo.17678252Keywords:
Edge, Computing, Embedded, AI, Systems, Power, Management, System-on-Chip, Architectures, Thermal, ConstraintsAbstract
Power management is a major challenge for embedded AI systems at the network edge. These systems must run machine learning workloads under tight energy limits.
An effective solution needs a multi-layered approach. Key elements include the Power State Coordination Interface (PSCI), secure firmware, and Linux kernel features for runtime control. Core techniques such as dynamic frequency scaling, clock gating, suspend/resume, and memory or accelerator-specific optimizations further improve efficiency.
Environmental factors add to the challenge. Automotive and industrial systems must meet strict thermal limits. Battery-powered devices face even tighter energy budgets. Both require adaptive control strategies.
From a software perspective, effective methods include specialized kernel drivers, standardized power APIs, and optimizations such as dynamic logic gating and real-time power monitoring. When combined, these enable power-efficient AI systems that maintain reliable performance while staying within thermal and energy boundaries.
References
Jonathan G Koomey et al., "Implications of Historical Trends in the Electrical Efficiency of Computing," ResearchGate, 2011. Available: https://www.researchgate.net/publication/224128141_Implications_of_Hist orical_Trends_in_the_Electrical_Efficiency_of_Computing
Pedro García López et al., "Edge-centric Computing: Vision and Challenges," ResearchGate, 2015. Available: https://www.researchgate.net/publication/282434420_Edge-centric_Computing
ARM Developer, "Arm Power State Coordination Interface Platform Design Document". Available: https://developer.arm.com/documentation/den0022/latest/
Robert Love, "Linux Kernel Development," Addison-Wesley, 2010. Available: https://www.doc-developpement-durable.org/file/Projets-informatiques/cours-&-manuels-informatiques/Linux/Linux%20Kernel%20Development,%203rd%20Edition.pdf
Dominik Brodowski, "CPUFreq - CPU frequency and voltage scaling code in the Linux(TM) kernel," Kernel. Available: https://docs.kernel.org/cpu-freq/index.html
Paul E. McKenney, "Is Parallel Programming Hard, And, If So, What Can You Do About It?" arXiv:1701.00854v6, 2023. Available: https://arxiv.org/abs/1701.00854
Massoud Pedram and Shahin Nazarian, "Thermal Modeling, Analysis, and Management in VLSI Circuits: Principles and Methods," IEEE, 2006. Available: https://weble.upc.edu/ifsin/Block5/paper_proc2.pdf
Parthasarathy Guturu and Bharat Bhargava, "Cyber-Physical Systems: A Confluence of Cutting Edge Technological Streams". Available: https://www.cs.purdue.edu/homes/bb/CPSReviewPaper.pdf
Jonathan Corbet et al., "Linux Device Drivers," lwn.net. Available: https://lwn.net/Kernel/LDD3/
Greg Kroah-Hartman, "Linux Kernel in a Nutshell," O’Reilly, 2006. Available: https://theswissbay.ch/pdf/ Gentoomen%20Library/Operating%20Systems/Linux/O%27
Reilly%20Linux%20Kernel%20in%20a%20Nutshell.pdf
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