FlowPrecision: Advancing FPGA-Based Real-Time Fluid Flow Estimation with Linear Quantization
This addresses the challenge of precise real-time fluid flow measurement for industrial and environmental monitoring, offering an efficient alternative to cloud-based processing in autonomous systems.
The study tackled real-time fluid flow estimation by applying linear quantization in FPGA-based soft sensors, achieving up to a 10.10% reduction in Mean Squared Error and a 9.39% improvement in inference speed.
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.