More than Moore with Neuromorphic Computing Architectures
(University of Heidelberg)
SB1 1.120 (GSI Main Lecture Hall)
GSI Main Lecture Hall
Neuromorphic computing systems represent a departure from the von Neumann architecture. They implement aspects of form and function observed in biological neural circuits. Potential advantages include energy efficiency, fault tolerance, compactness and, most importantly, the ability to learn by interaction with the environment.
Applications are in two areas : Improving the understanding of biological systems and cognitive computing to analyze causal relations and complex data and to make predictions. In the colloquium I will review current approaches in neuromorphic computing and discuss current and future use cases.
Reading suggestions for the enthusiastic public:
Learning : https://arxiv.org/abs/1604.05080
Mixing : https://arxiv.org/abs/1709.08166
Noise : https://arxiv.org/abs/1710.04931
Dendrites : https://arxiv.org/abs/1703.07286
Some experiments : https://arxiv.org/abs/1703.06043
Sampling : https://arxiv.org/abs/1311.3211