Neuroscience, Artificial Intelligence, Neuromorphic Engineering
Deep Learning Accelerators, Edge AI, Tiny-ML
Neuro-AI, State-Of-The-Art AI, Bio-inspired Neural Networks
Google Scholar
Research Group Leader
Zurich Research Center
Zürich, Switzerland
Previously:
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Research Contributions
& Publications
AI Algorithms
Improving AI & ML state of the art
in Deep Learning
Showing advantages of neuromorphic & neuro-AI surpassing conventional AI
through neuroscience and biological mechanisms
(neuro-AI).
by systematically identifying the unique functionality of neuromorphic & the suitable tasks.
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Beyond energy efficiency. Other advantages:
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Beyond just spikes. Other mechanisms:
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Advantages even on GPUs, without specialized hardware.
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In real-world tasks (e.g. Atari games, ImageNet, keyword spotting, robotics, etc.).
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Beyond neuromorphic niches, into mainstream AI.
AI Hardware
Applications of neuromorphic hardware & AI accelerators
Hardware design
for neuromorphic &
in-memory computing
by mapping algorithms to hardware.
by exploiting nanotechnology and material physics.
Neuroscience
Computational neuroscience
Systems neuroscience
through math & simulations.
function emerging from physiology.
by animal electrophysiology, psychophysics, and system-level models.
Click on each link below for publications on each topic.
Or click here to list all publications.