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

arXiv: https://arxiv.org/abs/2403.01922 · HTML: https://mlsanity.com/paper/2403.01922/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 12 references that are in the map.
- First posted on arXiv 4 Mar 2024.

## Builds on (most cited first, 5 of 5)

- [Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference](https://mlsanity.com/paper/1712.05877/) (2017)
- [Quantizing deep convolutional networks for efficient inference: A whitepaper](https://mlsanity.com/paper/1806.08342/) (2018)
- [Understanding Straight-Through Estimator in Training Activation Quantized Neural Nets](https://mlsanity.com/paper/1903.05662/) (2019)
- [Trained Quantization Thresholds for Accurate and Efficient Fixed-Point Inference of Deep Neural Networks](https://mlsanity.com/paper/1903.08066/) (2019)
- [On-Device Soft Sensors: Real-Time Fluid Flow Estimation from Level Sensor Data](https://mlsanity.com/paper/2311.15036/) (2023)

## Built on by (most cited first, 5 of 5)

- [Integer-only Quantized Transformers for Embedded FPGA-based Time-series Forecasting in AIoT](https://mlsanity.com/paper/2407.11041/) (2024)
- [Data-driven Modeling of Combined Sewer Systems for Urban Sustainability: An Empirical Evaluation](https://mlsanity.com/paper/2408.11619/) (2024)
- [Towards Auto-Building of Embedded FPGA-based Soft Sensors for Wastewater Flow Estimation](https://mlsanity.com/paper/2407.05102/) (2024)
- [Evaluating Time Series Models for Urban Wastewater Management: Predictive Performance, Model Complexity and Resilience](https://mlsanity.com/paper/2504.17461/) (2025)
- [Enabling Vibration-Based Gesture Recognition on Everyday Furniture via Energy-Efficient FPGA Implementation of 1D Convolutional Networks](https://mlsanity.com/paper/2510.23156/) (2025)

## Abstract (arXiv)

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.