The Concept of Self-Adaptive Dynamic Reconfiguration in FPGA-Based DSP Systems

This article presents the concept of digital signal processing (DSP) systems implemented in FPGA (Field-Programmable Gate Array) devices, utilizing Dynamic Partial Reconfiguration (DPR) mechanisms. It describes hardware architectures that enable selective replacement of logic configuration fragments in real time. Applications of DPR are proposed for the implementation of Fast Fourier Transform (FFT), adaptive filtering, and sound signal synthesis, where the choice of algorithm and its parameters can be dynamically adjusted based on the quality requirements of the processed signal and hardware resource constraints. Additionally, the use of adaptive mechanisms, including artificial intelligence, is discussed as a means of supporting the configuration selection process based on quality test results such as Signal-to-Noise Ratio (SNR), latency, processing time and energy consumption. The article explores the potential of using evolutionary algorithms, large language models (LLMs), and reinforcement learning for the generation and optimization of hardware configurations. The integration of the hardware layer (Intel and Xilinx FPGA) with the software layer is also presented, using Tool Command Language (TCL) scripts and AI systems. Methods for evaluating the quality of DSP algorithm implementation are proposed. The presented solutions make it possible to build digital signal processing systems that automatically adapt their hardware architecture to improve signal processing quality while minimizing resource usage.