Model Optimization Techniques for Embedded Artificial Intelligence

Deep neural networks (DNNs) are playing an important role in various areas including computer vision (CV) and natural language processing. This paper comprehensively analyzes model optimization techniques for deploying deep neural networks on resource-constrained embedded systems. We evaluate three core paradigmspruning, quantization, and dynamic inferencefocusing on their efficacy in balancing computational efficiency, memory footprint, and accuracy retention. For each technique, we conduct dedicated experiments spanning representative architectures including ResNet, VGG, Inception, and MobileNet variants to evaluate accuracy-FLOPS trade-offs. We also discuss and compare the practical deployment metrics for optimization techniques. Finally, we emphasize promising future directions.