Machine learning in hardware via trained metasurface encoders: theory, design and applications Maksim Makarenko, Ph.D. Student, Electrical and Computer Engineering Nov 23, 10:00 - 12:30 B2 L5 R5209 machine learning artificial intelligence photonics In this thesis, we introduce a novel concept of metasurface optical accelerators for machine learning with the corresponding end-to-end optimization framework that is robust to fabrication intolerance and can simultaneously optimize in tens of millions of degrees of freedom. The core of this technology is universal approximators, a single surface of optical nanoresonators mathematically equivalent to a single layer of an artificial neural network (ANN).
