Energy Efficient Online Stream Classification under Concept Drift on FPGAs for Edge Computing
Jonas Vaquet, Florian Porrmann, Sarah Pilz, Valerie Vaquet, Jens Hagemeyer, Ulrich Rückert, Barbara Hammer · 2025
With the increasing availability of data collected by edge devices over time, efficient algorithms running remotely on low-energy devices such as FPGAs are required. This includes Machine Learning algorithms, which constitute a valuable tool when analyzing and processing vast amounts of data. To keep accurate models under distributional changes, commonly referred to as concept drift, adaptive online learning models are required. While first works proposed FPGA implementations of several machine learning algorithms, in this work, we will focus on online learning using the neighbor-based SAM-kNN model, which showed good performance under heterogenous drifts. We propose an efficient FPGA implementation that yields considerable speed and energy efficiency advantages while keeping a competitive accuracy over a range of artificial and real-world benchmarks.The implementation code is available on GitHub at https://github.com/jvaquet/SAMkNN-on-FPGA.