학술논문
FlowPrecision: Advancing FPGA-Based Real-Time Fluid Flow Estimation with Linear Quantization
Document Type
Conference
Source
2024 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops) Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops), 2024 IEEE International Conference on. :733-738 Mar, 2024
Subject
Language
ISSN
2766-8576
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.