Developing New AI Model Compression Techniques

The rapid growth of artificial intelligence (AI) model complexity has created significant challenges for deployment on resource-constrained devices and customization by developers. Model compression techniques, such as pruning, quantization, and knowledge distillation, have emerged as critical solutions to reduce computational and memory demands while preserving accuracy. This work explores foundational and state-of-the-art approaches to AI model compression, emphasizing their role in enabling efficient edge computing, lowering energy consumption, and democratizing access to advanced AI capabilities. We discuss the trade-offs between model size, inference speed, and accuracy, and evaluate methods for deploying compressed models on mobile and IoT devices. Case studies on architectures like MobileNet and SqueezeNet demonstrate practical successes, while benchmark datasets and evaluation metrics highlight the need for standardized methodologies to assess compression efficacy. The paper concludes with guidelines for reliable experimentation and future directions in optimizing model efficiency without compromising performance. Keywords AI Model Compression, Pruning and Sparsity, Quantization, Knowledge Distillation, MobileNet, Edge Computing, Resource-Constrained Devices, Inference Optimization.