Real-Time Adaptive Neural Network on FPGA: Enhancing Adaptability through Dynamic Classifier Selection

Abstract

This research studies an adaptive neural network with a Dynamic Classifier Selection framework on Field-Programmable Gate Arrays (FPGAs). The evaluations are conducted across three different datasets. By adjusting parameters, the architecture surpasses all models in the ensemble set in accuracy and shows an improvement of up to 8% compared to a singular neural network implementation. The research also emphasizes considerable resource savings of up to 109.28%, achieved via partial reconfiguration rather than a traditional fixed approach. Such improved efficiency suggests that the architecture is ideal for settings limited by computational capacity, like in edge computing scenarios. The collected data highlights the architecture's two main benefits: high performance and real-world application, signifying a notable input to FPGA-based ensemble learning methods.