FPGA-Accelerated Fast Machine Learning for Heterogeneous Edge Systems

The integration of Fast Machine Learning (FastML) algorithms with edge devices for real-time building management system (BMS) poses challenges due to resource constraints and latency requirements. Addressing these challenges necessitates not only the quantization and optimization of ML models to achieve rapid inference but also their adaptation to fit within the limited resources of edge devices, thereby reducing computational overhead while maintaining predictive accuracy. These advancements are critical for enabling key functionalities for applications related to energy management, HVAC control, and fault detection in BMS applications. This study proposes an end-to-end edge-based framework utilizing **hls4ml** for the deployment of machine learning models on FPGA platforms, designed to process real-time building sensor data streams efficiently. By employing dynamic quantization and pruning techniques, the framework ensures the optimal use of FPGA resources, achieving low-latency inference without compromising model performance. The results underscore the potential of FPGA-accelerated ML systems in meeting the demands of real-time BMS applications, offering enhanced energy efficiency, operational reliability, and scalability. This work provides valuable insights into the evolving landscape of edge computing for smart building applications and highlights the broader implications for FPGA-based ML deployments in resource-constrained environments.