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This is a Literature Review study.

“Resource-Aware Deep Learning: Neural Network Optimization for Edge Devices: A Review”

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This review identifies methods optimizing neural networks in edge devices, highlighting energy-efficient deep learning strategies.

Energy-efficient deep learning significantly enhances the deployment of neural networks on resource-constrained edge devices.
Model compression, pruning, and quantization effectively reduce computational costs and maintain accuracy in neural networks.
Lightweight architectures like MobileNet and EfficientNet improve the feasibility of real-time inference on edge hardware.
Challenges remain in maintaining accuracy while balancing efficiency, especially against hardware variability and adversarial threats.

Chakraborty et al. (2025) studied this question.

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View Full Paper

Ask AI

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Authors

This review identifies methods optimizing neural networks in edge devices, highlighting energy-efficient deep learning strategies.

Energy-efficient deep learning significantly enhances the deployment of neural networks on resource-constrained edge devices.
Model compression, pruning, and quantization effectively reduce computational costs and maintain accuracy in neural networks.
Lightweight architectures like MobileNet and EfficientNet improve the feasibility of real-time inference on edge hardware.
Challenges remain in maintaining accuracy while balancing efficiency, especially against hardware variability and adversarial threats.

Chakraborty et al. (2025) studied this question.

View Full Paper

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Bookmark