Dynamic Neural Networks for Adaptive Edge AI: Techniques, Tools, and Development Directions

Edge artificial intelligence (Edge AI) is increasingly vital in smart surveillance, industrial IoT, and autonomous systems, where real-time processing and energy efficiency are critical. Traditional deep neural networks are accurate but static and resource-hungry, limiting deployment on constrained devices. Dynamic neural networks enable input-adaptive computation, adjusting workload at runtime to context, input difficulty, and hardware limits. This survey synthesizes methods that support dynamic adaptability alongside offline compression (e.g., pruning, quantization) and runtime strategies (e.g., early exiting, dynamic resolution control). We review supporting software frameworks, including Slimmable Networks, Once-for-All (OFA), and PyTorch-based modules for adaptive execution. Finally, we outline a research agenda to co-optimize pre-deployment compression with runtime adaptation, targeting accuracy, latency, energy trade offs for robust, real-world edge deployment.