FlowPrecision: Advancing FPGA-Based Real-Time Fluid Flow Estimation with Linear Quantization
<|reference_start|>FlowPrecision: Advancing FPGA-Based Real-Time Fluid Flow Estimation with Linear Quantization: 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.<|reference_end|>
@article{ling2024flowprecision:,
    title={FlowPrecision: Advancing FPGA-Based Real-Time Fluid Flow Estimation with
  Linear Quantization},
    author={Tianheng Ling, Julian Hoever, Chao Qian, Gregor Schiele},
    journal={arXiv preprint arXiv:2403.01922},
    year={2024},
    doi={10.1109/PerComWorkshops59983.2024.10503436},
    archivePrefix={arXiv},
    eprint={2403.01922},
    primaryClass={cs.LG physics.flu-dyn}
}