Dresden 2020 – scientific programme

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SYNC: Symposium Advanced neuromorphic computing hardware: Towards efficient machine learning

SYNC 1: Advanced neuromorphic computing hardware: Towards efficient machine learning

SYNC 1.3: Invited Talk

Monday, March 16, 2020, 10:30–11:00, HSZ 01

Towards brain-inspired photonic computing — •Wolfram Pernice — University of Münster, Münster, Germany

Photonic integrated circuits allow for designing computing architectures which process optical signals in analogy to electronic integrated circuits. Therein electrical connections are replaced with photonic waveguides which guide light to desired locations on chip. Through near-field coupling, such waveguides enable interactions with functional materials placed very close to the waveguide surface. This way, photonic circuits which are normally passive in their response are able to display active functionality and thus provide the means to build reconfigurable systems. By integrating phase-change materials nonvolatile components can be devised which allow for implementing hardware mimics of neural tissue. Here I will present our efforts on using such a platform for developing optical non-von Neumann computing devices. In these reconfigurable photonic circuits in-memory computing allows for overcoming separation between memory and central processing unit as a route towards artificial neural networks which operate entirely in the optical domain.