Paper accepted in FPT 2023

Congratulations to Frank Ridder and Nikolaos Alachiotis that our paper “Accelerated Real-Time Classification of Evolving Data Streams using Adaptive Random Forests” is accepted by FPT 2023!

Machine learning is increasingly applied to a wide range of real-time applications, with classification tasks playing a critical role in enabling intelligent decision-making. However, the phenomenon of concept drift, where the underlying data distribution changes over time, presents a significant challenge to maintaining the accuracy of machine learning models in applications with evolving data streams such as health monitoring or sensor-data analyses.

The Adaptive Random Forest (ARF), as one prominent algorithm addresses this issue by coupling multiple Hoeffding Trees with a drift detector to adapt to concept drift. As training a forest of growing decision trees is a high-latency operation, custom-hardware acceleration is needed to meet the stringent latency requirements for real-time use of ARF. This work describes the first FPGA implementation of the ARF algorithm, focusing on achieving high hardware efficiency, scalability, and adaptability for four different datasets.

Kuan-Hsun Chen
Kuan-Hsun Chen
Assistant Professor

In this era, I make embedded systems future-proof and future-ready.