Deep-to-Shallow Neural Networks: A Promising Approach for Embedded AI

This ArXiv paper explores a novel architecture for neural networks adaptable to the resource constraints of embedded systems. The research offers insights into optimizing deep learning models for deployment on devices with limited computational power and memory.

• •Focuses on adapting deep learning models for resource-constrained environments.
• •Proposes a new architecture leveraging transformable neural networks.
• •Aims to improve efficiency and performance in embedded AI applications.