Optimized Neural Network Deployment Strategies for Edge Computing Environments

Abstract

Edge computing environments present unique challenges for neural network deployment due to resource constraints and latency requirements. This paper explores optimized deployment strategies for neural networks in edge computing scenarios, focusing on model compression techniques, adaptive allocation algorithms, and dynamic resource management. We propose a novel framework that combines quantization, pruning, and knowledge distillation to create lightweight models without significant accuracy loss. Experimental results demonstrate that our approach reduces model size by up to 70% while maintaining 95% of original accuracy. The framework also includes an adaptive scheduler that dynamically redistributes computational loads based on current network conditions and task priorities. Our evaluation across multiple edge devices shows an average latency reduction of 40% compared to traditional deployment methods. These findings contribute to more efficient and practical implementations of artificial intelligence in resource-constrained environments, enabling real-time applications in IoT, autonomous systems, and smart cities.