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

• URL: http://arxiv.org/abs/2403.01922v2
• Date: Thu, 20 Jun 2024 09:03:17 GMT
• Title: FlowPrecision: Advancing FPGA-Based Real-Time Fluid Flow Estimation with Linear Quantization
• Authors: Tianheng Ling, Julian Hoever, Chao Qian, Gregor Schiele,
• Abstract summary: This study applies linear quantization in FPGA-based soft sensors for fluid flow estimation. Our approach achieves up to a 10.10% reduction in Mean Squared Error and a notable 9.39% improvement in inference speed.
• Score: 18.15754187896287
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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