Design Space Exploration for PCA Implementation of Embedded Learning in FPGAs.
2018 IEEE International Symposium on Circuits and Systems (ISCAS)(2018)
摘要
Nowadays, the growth of Industry 4.0 and Internet of Things (IoT) demands new solutions for designing low-power low-cost advanced computational algorithms. This work develops the sensor signal processing layer of a chemical biosensing IoT edge device using NanoPillar transducers. We propose to move from smart sensors to expert sensors, applying Principal Component Analysis (PCA) for dimensionality reduction in FPGAs. As a result, this paper provides a design space exploration of PCA implementation over FPGAs, studying parameters as throughput and resource usage.
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关键词
Principal Component Analysis,Dimensionality Reduction,Expert Sensors,Biosensor,Machine Learning,FPSoC
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