Edge Computing and Neural Network Accelerators: Optimization and Future Development of Artificial Intelligence Technology

Edge computing and neural network accelerators are revolutionizing the landscape of artificial intelligence and data processing by solving critical issues such as latency, energy efficiency, and real-time computing. Edge computing deploys computing resources near data sources, optimizes data transmission and processing, and is widely used in fields such as smart cities, industrial Internet of Things, and telemedicine. Neural network accelerators use dedicated hardware (such as ASICs and FPGAs) to significantly improve the performance of deep learning models and overcome the bottlenecks of traditional computing architectures.This article discusses the integration of artificial intelligence technology in edge computing, focusing on lightweight model optimization technologies such as quantization, pruning, and memory optimization. These technologies promote the efficient deployment of artificial intelligence on resource-constrained devices, laying the foundation for transformative applications in areas such as safety monitoring, medical diagnosis, and autonomous systems. In addition, this paper examines innovations in hardware architecture, such as photon accelerators and modular design, which further improve system adaptability and scalability.Despite significant progress, resource constraints, security, and scalability remain major challenges. This article summarizes the latest developments in edge computing frameworks, artificial intelligence technology integration, and neural network accelerators, and analyzes the potential of these technologies in different application fields and their future development directions. The research results provide a reference path for promoting the development of edge computing-based artificial intelligence technology in future applications.