May Seminar: Computing with Physical Systems—May 13

Peter McMahon

    Assistant Professor of Applied & Engineering Physics  
    Cornell University 

    Date: May 13, 2024
    Time: 3:00 - 4:00 PM ET
    Location:  MIT.nano 12-0168 (basement) or via Zoom.
   Reception to follow
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Conventional digital computing technology based on complementary metal–oxide–semiconductor (CMOS) devices has accumulated a performance increase in excess of 1,000,000x versus the first CMOS digital processors. However, the demand for more computing power (and simultaneously increased energy efficiency)—especially for neural networks and for processing a deluge of sensor data—has increased far more rapidly than what even the already brisk pace of advances in modern processors and special-purpose accelerator chips have been able to deliver. How can we meet this demand over the next decade and beyond?

Professor McMahon's group studies how by relaxing or breaking some of the tenets of modern computing—such as digital signals, deterministic behavior, computational universality, and a hierarchy of abstractions from digital logic through to software—and reimagining how computers are built at the fundamental level of physical dynamics, we may be able to make orders-of-magnitude leaps in speed or efficiency in specialized co-processors.

In this talk, Professor McMahon will briefly introduce the general research program of physics-based computing and then focus on a concrete example, physical neural networks [1]. He will describe a method his group has developed to train any complex physical system to perform as a neural network for machine-learning tasks, and then give an application of this method to a new kind of on-chip photonic neural processor his lab has developed, one whose refractive index as a function of space they can reprogram with light [2]. He will also speculate about possible implementations of nanoelectronic physical neural networks and discuss the potential of physical neural networks for smart sensors that pre-process acoustic, microwave or optical signals in their native domain before digitization [3]. 

[1] L.G. Wright*, T. Onodera* et al. Nature 601, 549-555 (2022)

[2] T. Onodera*, M. Stein* et al. arXiv:2402.17750 (2024)

[3] T. Wang*, M. Sohoni* et al. Nature Photonics 17, 408 (2023)


Peter McMahon is an assistant professor of 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 Program Award and a Google Quantum Research Award, and is a CIFAR Azrieli Global Scholar in Quantum Information Science.