How to Cite:
Dr. Hiroshi Tanaka, "Energy-Aware AI Scheduling for Resource-Constrained Edge Devices" American International Journal of Computer Science and Technology, Vol. 1, No. 1, pp. 7-13, 2025.

Abstract:
As artificial intelligence (AI) applications continue to proliferate at the edge of networks, ensuring efficient utilization of limited computational and energy resources has become critical. This paper proposes a novel energy-aware AI scheduling framework tailored for resource-constrained edge devices. Our approach dynamically allocates computational tasks based on energy consumption models, workload characteristics, and system performance constraints. We integrate lightweight profiling techniques with real-time scheduling algorithms to balance energy efficiency and task accuracy. Experimental results on representative edge hardware platforms show that our method reduces energy consumption by up to 35% while maintaining comparable AI performance, outperforming traditional fixed-scheduling approaches. These findings highlight the potential of intelligent scheduling strategies in enabling sustainable and scalable edge AI deployment.

Keywords: Edge AI, Energy-Aware Scheduling, Resource-Constrained Devices, Real-Time Systems, Task Scheduling, Energy Efficiency, Embedded AI, Edge Computing, Dynamic Workload Management.

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