Abstract: While traditional computers can simulate the function of the biological brain, this comes at a huge disadvantage in terms of speed and energy use. However, directly emulating the structure of the brain in hardware is extremely challenging due to the very high physical fanout, localized memory and analog/digital hybrid computation. In this talk, we will introduce ideas on hardware for understanding the brain and building artificial intelligence. We will discuss how a novel hardware platform, the superconducting opto-electronic hardware platform, can be used to directly emulate some of the features of the brain in hardware. The platform comprises semiconductor LED light sources coupled to integrated waveguides for communication, and superconducting single photon detectors and superconducting electronics for low-power, energy efficient computation. We will finally discuss future directions that might take advantage of recent work on algorithms at the intersection of machine learning and neuroscience, and how opto-electronic hardware may contribute to physical implementation of these new algorithms.

Bio: Dr. Sonia Buckley is a physicist at the National Institute of Standards and Technology (NIST) in Boulder, Colorado in the group of Dr. Richard Mirin and Dr. Sae Woo Nam. Sonia received a PhD in Applied Physics and an MS in Electrical Engineering from Stanford University in 2014, and her undergraduate degree in Physics from Trinity College Dublin in 2009. Her doctoral work was done under the supervision of Prof. Jelena Vuckovic on nonlinear frequency conversion in III-V photonic crystal cavities. Her current research interests are in the integration of photonic/opto-electronic devices with superconducting electronics for applications in integrated quantum optics and artificial intelligence.