Exploring Scalability of Deep Neural Networks for Real-Time Video Processing in Edge Devices

Abstract

Applications like augmented reality, smart surveillance, and autonomous driving have made real-time video processing on edge devices more and more necessary. However, because of the restricted processing resources, memory limitations, and latency requirements, installing deep neural networks (DNNs) on edge devices presents major hurdles. the scalability of DNN architectures designed with the goal of maximizing model size, processing speed, and accuracy for real-time video processing on edge devices. We examine a number of methods, such as lightweight network designs, quantization, and model pruning, to determine how well they can lower computational load without sacrificing speed. According to experimental results, DNNs can significantly reduce latency and power consumption through smart tuning, allowing for effective real-time processing on devices with restricted resources. route toward high-performance, scalable DNN models, offering valuable perspectives on realistic deployment tactics for edge-based video processing applications.