FlowPrecision: Advancing FPGA-Based Real-Time Fluid Flow Estimation with Linear Quantization
Fuente:
arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866908612999249920 |
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| author | Ling, Tianheng Hoever, Julian Qian, Chao Schiele, Gregor |
| author_facet | Ling, Tianheng Hoever, Julian Qian, Chao Schiele, Gregor |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_01922 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | FlowPrecision: Advancing FPGA-Based Real-Time Fluid Flow Estimation with Linear Quantization Ling, Tianheng Hoever, Julian Qian, Chao Schiele, Gregor Machine Learning Fluid Dynamics 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. |
| title | FlowPrecision: Advancing FPGA-Based Real-Time Fluid Flow Estimation with Linear Quantization |
| topic | Machine Learning Fluid Dynamics |
| url | https://arxiv.org/abs/2403.01922 |