Design Space Exploration for PCA Implementation of Embedded Learning in FPGAs

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.