Optimizing Deep Learning Models for RealTime Applications: Techniques for Improving Inference Speed without Sacrificing Accuracy

Abstract

In recent years, the rapid advancement of deep learning models has enabled significant breakthroughs in various fields, including computer vision, natural language processing, and autonomous systems. However, deploying these models in real-time applications remains a challenge due to the high computational demands and latency constraints. This thesis explores techniques to optimize deep learning models for real-time applications by improving inference speed without sacrificing accuracy. The study begins by analyzing the fundamental trade-offs between model complexity and performance, and then investigates a range of optimization strategies, such as model pruning, quantization, knowledge distillation, and efficient neural architectures. Experimental evaluations are conducted across several real-world scenarios, including image and video processing, speech recognition, and autonomous navigation, demonstrating the effectiveness of these techniques. The results show that with appropriate optimization, deep learning models can achieve significant reductions in latency and computational load while maintaining high accuracy, making them viable for deployment in real-time environments. This work contributes to the growing body of knowledge in the field of efficient AI and provides a comprehensive framework for practitioners aiming to enhance the performance of deep learning systems in time-sensitive applications.