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However, these AI models have high computational demands, and thus high energy consumption, limiting scalability in power-constrained environments. This paper systematically addresses energy optimization in real-time video inference by exploring energy-performance trade-offs with sophisticated AI models, i.e., YOLOv8 and MobileNet. A variety of techniques, such as model pruning, quantization, and hardware-aware optimizations, were stringently evaluated. Experimental results indicate that model pruning resulted in a reduction in energy expenditure by up to 35% without compromising detection accuracy to 92%. Moreover, quantization resulted in a further energy saving by 18% and improved inference acceleration by 25% with minimal reduction in accuracy. Moreover, reducing frame rates from 30 frames per second (FPS) to 15 FPS resulted in a power reduction by 40% with a mere reduction in detection performance by 3%. Benchmarking across different hardware setups showed that power savings by optimized lightweight models running in edge devices were by as much as 50% compared to GPUs. These results highlight feasible directions for energy efficient AI system design in real-time video applications, which prove to be particularly useful in edge computing and Internet of Things environments.<\/jats:p>","DOI":"10.1007\/s11554-025-01703-0","type":"journal-article","created":{"date-parts":[[2025,6,16]],"date-time":"2025-06-16T15:01:14Z","timestamp":1750086074000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Energy-aware deep learning for real-time video analysis through pruning, quantization, and hardware optimization"],"prefix":"10.1007","volume":"22","author":[{"given":"M. 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