AI-Enabled Hardware–Software Co-Design for Real-Time Data Analytics in Cloud-Connected Cyber-Physical Systems

Abstract

Due to the rapid development of cloud-based Cyber-Physical Systems (CPS) and smart hardware, a large volume of industrial data has been generated, posing challenges related to latency, scalability, and effective hardware-software integration. The paper provides a framework for an AI-enabled hardware-software co-design of real-time CPS data analytics. It is proposed that the smart sensor hardware, edge-assisted processing, and machine learning models are integrated into the proposed system to enable low-latency decision-making and optimal system performance. The co-design technique guarantees effective communication between hardware potential and software intelligence, reducing computational overhead and communication delays. The proposed framework is evaluated on 10,000 records, showing 91.3% prediction accuracy, 88.7% processing efficiency, 0.87% system stability, and a low computational latency of 198 ms. In addition, the general performance index is 0.89 and it demonstrates the balanced scaling, responsiveness and efficiency. The superiority demonstrated by comparative analysis over traditional, machine-learning-based, and hybrid models indicates that the proposed model is the best approach for real-time industrial analytics in dynamic CPS settings.