tinyML Talks – recorded December 8, 2020
“Training Embedded AI/ML Using Synthetic Data”
Ian Campbell – OnScale
In the past, engineers relied on physical testing to generate datasets to train embedded AI and ML algorithms. Today, engineers at world-class companies are using Cloud Simulation to generate “synthetic” datasets for training embedded AI/ML. Cloud Simulation empowers engineers to run massive parametric sweeps of things like sensors within a system that are subjected to variances in manufacturing and environmental operating conditions. Simulation also allows engineers and data scientists to control noise within the AI/ML dataset – either removing it entirely for baseline analysis or injecting various types of noise (e.g. thermal or vibration noise) into a sensor system to ensure AI/ML algorithms are robust against noise.
