Edge-Optimized Neural Network Architecture for Real-Time IoT Data Processing: A Hardware-Software Co-Design Approach

This paper presents a novel edge-optimized neural network architecture specifically designed for real-time Internet of Things (IoT) data processing. This approach integrates hardware acceleration techniques with software optimization strategies to achieve significant improvements in processing latency and energy efficiency. The proposed system demonstrates a 65% reduction in inference time and 40% decrease in power consumption compared to traditional cloud-based processing methods, while maintaining 99.2% accuracy relative to fullprecision baselines. The effectiveness of this proposed hardwaresoftware co-design methodology is based on comprehensive statistical analysis using embedded ARM Cortex-A78 processors with custom FPGA acceleration units across multiple experimental runs.