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.
- C. Qian, T. Ling, and G. Schiele, “Elasticai: Creating and deploying energy-efficient deep learning accelerator for pervasive computing,” in 2023 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops). IEEE, 2023, pp. 297–299.
- J. Tomperi, P. M. Rossi, and M. Ruusunen, “Estimation of wastewater flowrate in a gravitational sewer line based on a low-cost distance sensor,” Water Practice & Technology, p. wpt2022171, 2022.
- P. Yin, J. Lyu, S. Zhang, S. Osher, Y. Qi, and J. Xin, “Understanding straight-through estimator in training activation quantized neural nets,” arXiv preprint arXiv:1903.05662, 2019.
- N. Noori, T. Waag, H. Viumdal, R. Sharma, M. Jondahl, and A. Jinasena, “Non-newtonian fluid flow measurement in open venturi channel using shallow neural network time series and non-contact level measurement radar sensors,” in SPE Norway Subsurface Conference. OnePetro, 2020.
- B. Jacob, S. Kligys, B. Chen, M. Zhu, M. Tang, A. Howard, H. Adam, and D. Kalenichenko, “Quantization and training of neural networks for efficient integer-arithmetic-only inference,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2018.
- T. Ling, C. Qian, and G. Schiele, “On-device soft sensors: Real-time fluid flow estimation from level sensor data,” 2023.
- H. Liang, C. Song, Z. Li, and H. Yang, “Volume flow rate calculation model of non-full pipe multiphase flow based on ultrasonic sensors,” Physics of Fluids, vol. 35, no. 3, 2023.
- K. Chhantyal, H. Viumdal, S. Mylvaganam, and G. Elseth, “Ultrasonic level sensors for flowmetering of non-newtonian fluids in open venturi channels: Using data fusion based on artificial neural network and support vector machines,” in 2016 IEEE Sensors Applications Symposium (SAS). IEEE, 2016, pp. 1–6.
- R. Krishnamoorthi, “Quantizing deep convolutional networks for efficient inference: A whitepaper,” arXiv preprint arXiv:1806.08342, 2018.
- A. Pappalardo. (2023) Xilinx/brevitas. [Online]. Available: https://doi.org/10.5281/zenodo.3333552
- S. Jain, A. Gural, M. Wu, and C. Dick, “Trained quantization thresholds for accurate and efficient fixed-point inference of deep neural networks,” Proceedings of Machine Learning and Systems, pp. 112–128, 2020.
Paper Prompts
Sign up for free to create and run prompts on this paper using GPT-5.
Top Community Prompts
Collections
Sign up for free to add this paper to one or more collections.
