Adaptive K values and training subsets selection for optimal K-NN performance on FPGA

Abstract

This study introduces an Adaptive K-Nearest Neighbors methodology designed for FPGA platforms, offering substantial improvements over traditional K-Nearest Neighbors implementations. By integrating a dynamic classifier selection system, our approach enhances adaptability, enabling on-the-fly adjustments of K values and subsets of training data. This flexibility results in up to a 10.66% improvement in accuracy and significantly reduces latency, rendering our system up to 3.918 times more efficient than conventional K-Nearest Neighbors techniques. The methodology’s efficacy is validated through experiments across multiple datasets, demonstrating its potential in optimizing both classification accuracy and system efficiency. The adaptive approach’s ability to improve response times, along with its flexibility, positions it as an ideal solution for real-time applications and highlights the advantages of the adaptive K-Nearest Neighbors methodology in overcoming the constraints of hardware-accelerated machine learning.