Verification of Random Number Generators for Embedded Machine Learning

Embedded systemsare utilizing complex machine learningdesigns to solve difficult problems. It is a challenge to maximize the design efficiency with limitations to space, power, and heat generation. Random Number Generators(RNGs) must meet design constraints while also trying to be sufficiently random. We investigate the randomnessof multiple RNGs and explore how degrees of randomnessaffect machine learning.