Embedded systems are utilizing complex machine learning designs 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 randomness of multiple RNGs and explore how degrees of randomness affect machine learning.


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    Title :

    Verification of Random Number Generators for Embedded Machine Learning


    Contributors:


    Publication date :

    2018-07-01


    Size :

    833202 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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