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

Monday, March 16, 2020, 09:30–12:15, HSZ 01

Recently novel computational approaches such as neural networks are revolutionizing computation. At the same time, we experience that the performance growth of digital microchips is saturating and the energy consumption of classical digital electronic processors is becoming a serious issue. This impasse has re-invigorated learning from the brain with its amazing intelligence-per-watt ratio and the exploration of unconventional physical substrates and nonlinear phenomena.

Our symposium will present the recent progress and future perspectives of neuro-inspired computing based on solid state systems and its relation to machine learning. This includes not only important aspects of novel computational architectures in unconventional substrates but also new theoretical concepts of computing in non-digital, "brain-like" physical substrates.

The chosen topic has highly interdisciplinary as we aim at bringing together researchers from material science, machine learning, computer engineering, nonlinear dynamics with exciting talks of renowned international expertise in the field.

	09:30	SYNC 1.1	Invited Talk: Photonic Reservoir Computing and its Application to Optical Communication — •Ingo Fischer and Apostolos Argyris
	10:00	SYNC 1.2	Invited Talk: Metal-oxide resistance switching memory devices as artificial synapses for brain-inspired computing — •Sabina Spiga
	10:30	SYNC 1.3	Invited Talk: Towards brain-inspired photonic computing — •Wolfram Pernice
	11:00		15 min. break
	11:15	SYNC 1.4	Invited Talk: Photonic Recurrent Ising Sampler — •Charles Roques-Carmes, Yichen Shen, Cristian Zanoci, Mihika Prabhu, Fadi Atieh, Li Jing, Tena Dubček, Chenkai Mao, Miles Johnson, Vladimir Čeperić, John Joannopoulos, Dirk Englund, and Marin Soljačić
	11:45	SYNC 1.5	Invited Talk: Beyond von Neumann systems: Computational memory for efficient AI — •Irem Boybat