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arXiv · 2403.01922

FlowPrecision: Advancing FPGA-Based Real-Time Fluid Flow Estimation with Linear Quantization

Tianheng Ling·Julian Hoever·Chao Qian·Gregor Schiele
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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.

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Tianheng Ling, Julian Hoever, Chao Qian, Gregor Schiele. 2024-03-04. FlowPrecision: Advancing FPGA-Based Real-Time Fluid Flow Estimation with Linear Quantization. https://doi.org/10.1109/percomworkshops59983.2024.10503436

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