FlowPrecision: Advancing FPGA-Based Real-Time Fluid Flow Estimation with Linear Quantization
Abstract
In industrial and environmental monitoring, achieving real-time and precise fluid flow measurement remains a critical challenge. This study applies linear quantization in FPGA-based soft sensors for fluid flow estimation, significantly enhancing Neural Network model precision by overcoming the limitations of traditional fixed-point quantization. Our approach achieves up to a 10.10% reduction in Mean Squared Error and a notable 9.39% improvement in inference speed through targeted hardware optimizations. Validated across multiple data sets, our findings demonstrate that the optimized FPGA-based quantized models can provide efficient, accurate real-time inference, offering a viable alternative to cloud-based processing in pervasive autonomous systems.
Keywords
Cite
@article{arxiv.2403.01922,
title = {FlowPrecision: Advancing FPGA-Based Real-Time Fluid Flow Estimation with Linear Quantization},
author = {Tianheng Ling and Julian Hoever and Chao Qian and Gregor Schiele},
journal= {arXiv preprint arXiv:2403.01922},
year = {2025}
}
Comments
6 pages, 3 figures, The 22nd International Conference on Pervasive Computing and Communications (PerCom 2024), PerConAI Workshop