Computing with Physical Systems

[CFAR Distinguished Professor Lecture Series]
Computing with Physical Systems by Professor Peter McMahon
With conventional digital computing technology reaching its limits, there has been a renaissance in analog computing across a wide range of physical substrates. In this talk, Prof Peter McMahon from Cornell University will introduce the concept of Physical Neural Networks and describe a method that his group has developed to train any complex physical system to perform as a neural network for machine-learning tasks. They have been able to show MNIST handwritten-digit classification experimentally on i) mechanical, ii) electronic and iii) photonic systems, despite the fact that none were initially designed to do machine learning. He will also describe several possible future research directions on Physical Neural Networks, including: 

i) the potential to create large-scale photonic accelerators for server-side machine learning, 
ii) smart sensors that pre-process acoustic, microwave or optical signals in their native domain before digitisation,
iii) new kinds of quantum neural network that does not require a carefully engineered quantum computer, 
iv) and generally the prospect to endow analog physical systems with new, unexpected functionality.

Prof Peter McMahon is an assistant professor in Applied and Engineering Physics at Cornell University, where he has been since 2019. Prior to joining Cornell, he completed his Ph.D. in Electrical Engineering and postdoctoral training in Applied Physics at Stanford University. He is the recipient of Packard and Sloan Fellowships, an Office of Naval Research Young Investigator Programme Award and a Google Quantum Research Award, and is a CIFAR Azrieli Global Scholar in Quantum Information Science.