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FlowPrecision: Advancing FPGA-Based Real-Time Fluid Flow Estimation with Linear Quantization
- Publication Year :
- 2024
-
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.<br />Comment: 6 pages, 3 figures, The 22nd International Conference on Pervasive Computing and Communications (PerCom 2024), PerConAI Workshop
- Subjects :
- Computer Science - Machine Learning
Physics - Fluid Dynamics
Subjects
Details
- Database :
- arXiv
- Publication Type :
- Report
- Accession number :
- edsarx.2403.01922
- Document Type :
- Working Paper
- Full Text :
- https://doi.org/10.1109/PerComWorkshops59983.2024.10503436