FlowPrecision: Advancing FPGA-Based Real-Time Fluid Flow Estimation with
  Linear Quantization
Tianheng Ling Julian Hoever Chao Qian Gregor Schiele
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.