FPGA-Accelerated Fast Machine Learning for Heterogeneous Edge Systems - Research presentation

Aug 3, 2026

Uploaded by Quinn

Fast Machine Learning (FastML) on edge devices for real-time building management faces constraints in compute capacity and strict latency limits. The work addresses these issues through quantization, optimization, and hardware-aware adaptation that reduce computation while preserving predictive accuracy. An end-to-end FPGA-based edgebased framework using hls4ml deploys ML models on FPGA platforms to process building sensor streams efficiently. Dynamic quantization and pruning target optimal FPGA resource use, enabling low-latency inference for energy management, HVAC control, and fault detection, improving efficiency and scalability in resource-constrained edge environments.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages9

Reading Time23 min

AccessFree to download

Introduction

Challenges in BMS with FastML

Limitations of cloud and CPU/GPU approaches

Why FPGAs for edge ML workloads

FPGA-based FastML framework with hls4ml

End-to-end edge deployment pipeline

Quantization and pruning for resource efficiency

FPGA-Accelerated Fast Machine Learning for Heterogeneous Edge Systems Mohammed Mshragi School of Engineering Cardiff University, UK [MshragiM@cardiff.ac.uk](MshragiM@cardiff.ac.uk) Ioan Petri School of Engineering Cardiff University, UK [petrii@cardiff.ac.uk](petrii@cardiff.ac.uk) Omer Rana School of Computer Science and Informatics Cardiff University, UK [ranaof@cardiff.ac.uk](ranaof@cardiff.ac.uk) Abstract—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 edgebased 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. Index Terms—FPGA, FastML, machine learning, building management systems, edge computing, acceleration, energy; I. INTRODUCTION The integration of Fast Machine Learning with Building Management Systems (BMS) can accelerate decarbonisationin the built environment and improve the demand response strategies in buildings. The possibility of developing Edge supported BMS capabilities using machine learning (ML) and edge computing can facilitate the development of a (near)real-time energy optimization capability for buildings but deploying these technologies in practice using resourceconstrained environments remains a significant challenge [1] . Traditional building energy management systems (BEMS) have relied heavily on rule-based control strategies, effective for simpler structures but inadequate for the complexity of modern buildings. Cloud-based solutions can process data from smart meters and IoT sensors but introduce challenges such as latency, data security concerns, high transfer costs, and operational expenses [2],[3]. These limitations are particularly problematic for real-time applications, where delays can hinder critical decision-making during demand response scenarios. Recent intelligent BMS solutions rely on ML models executed on centralized CPUs or GPUs, which achieve high prediction accuracy but can increase power consumption and introduce latency that impedes rapid responses during critical demand response (DR) scenarios. Field Programmable Gate Arrays (FPGAs) offer a compelling alternative, excelling in power efficiency and costeffectiveness compared to CPUs and GPUs for ML workloads. Research indicates that FPGAs can deliver comparable or superior performance while consuming less power [4]–[6] . However, deploying complex ML models on FPGAs typically requires extensive manual coding in Hardware Description Languages (HDLs) . Despite the potential of FPGA-based solutions, prior studies have attempted to address key inefficiencies. For instance, [7] implemented Model Predictive Control (MPC) using artificial neural networks (ANNs) for heating con

FAQs

What main problem does the study address in building management systems?

It targets the difficulty of deploying Fast Machine Learning on edge devices under resource limits and tight real-time latency requirements for BMS applications.

How does the proposed framework deploy ML models onto FPGA platforms?

It uses an end-to-end edgebased pipeline with hls4ml to transform and deploy ML models for efficient FPGA execution on streaming sensor data.

Which optimization techniques are used to improve FPGA inference efficiency?

Dynamic quantization and pruning are applied to the ML models to reduce FPGA resource usage while maintaining predictive performance.

Aug 3, 2026

Uploaded by Quinn