Efficient Deep Learning for Edge Devices: Optimizing Models for Resource-Constrained Environments

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This analysis reveals optimized model performance for edge devices, suggesting methods like quantization and knowledge distillation.

Optimizing model performance improves processing capabilities in resource-constrained edge environments, enhancing efficiency.
Key techniques include model trimming and quantization, addressing memory and computation challenges for deep learning.
Assessment focuses on methodologies such as lightweight structures and edge-cloud teaming, aiming for dynamic adaptability.
Emphasis on balanced trade-offs in energy efficiency and speed highlights ongoing challenges in edge intelligence deployment.

Rani et al. (2025) studied this question.

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