This comprehensive survey examines strategies for deploying high-performance AI models within the strict compute, memory, and energy limitations of edge devices.

Key Approaches to Edge AI Optimization:

• Model Compression Techniques: Pruning, quantization, tensor decomposition, and knowledge distillation to create smaller, faster models
• Resource-Aware Design: Methods for balancing performance with strict hardware constraints
• Performance-Efficiency Tradeoffs: Strategic approaches to maximize AI capabilities while minimizing resource usage

For engineering teams, this research provides critical insights into implementing sophisticated AI capabilities on resource-limited edge devices, enabling advanced applications in IoT, mobile computing, and embedded systems.

On Accelerating Edge AI: Optimizing Resource-Constrained Environments