Adaptive and Reconfigurable FPGA-Based Systems Architecture with Approach Machine Learning Model
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Abstract
The increasing complexity and variability of modern avionics systems require innovative architectures that can adapt to changing requirements while ensuring high performance and reliability. This paper presents a novel approach to designing adaptive and reconfigurable Field-Programmable Gate Array (FPGA)-based avionics architectures using machine learning (ML) techniques. Our proposed architecture integrates a ML-driven approach to dynamically reconfigure the FPGA’s resources, enabling the system to optimize its performance and power consumption in real-time. The Approach machine learning model is employed to predict the system’s workload and adjust the FPGA’s configuration accordingly. We demonstrate the effectiveness of our approach through a case study on a realistic avionics application, showcasing improved system adaptability, reduced latency, and enhanced reliability. The proposed architecture has significant implications for the development of future avionics systems, enabling them to efficiently respond to changing operational conditions while maintaining high performance and safety standards.