University of Cambridge > Talks.cam > Machine Learning @ CUED > Future technology: machine learning using memristors networks

Future technology: machine learning using memristors networks

Add to your list(s) Download to your calendar using vCal

If you have a question about this talk, please contact Christian Steinruecken.

I discuss the properties of general networks made of a class of memristors (resistors with memory) which are used for non-conventional computing. In fact, these components emulate the plasticity of neurons and can be fabricated in specialized laboratories. After having extensively introduced these components, we discuss a differential equation which describes the evolution of the internal memory of a generic circuit for ideal memristors. This enables a formal treatment of the learning capability of these circuits. I then discuss the implications of such an equation for the use of memristors in machine learning, showing that in a certain limit of the parameter space the dynamics can be interpreted as a constrained gradient descent. I will also give a brief account of the formal connection to Statistical Mechanics at the end.

This talk is part of the Machine Learning @ CUED series.

Tell a friend about this talk:

This talk is included in these lists:

Note that ex-directory lists are not shown.

 

© 2006-2020 Talks.cam, University of Cambridge. Contact Us | Help and Documentation | Privacy and Publicity