Towards Training Robust Computer Vision Models for Neuromorphic Hardware

Join Gregor Lenz as he delves into the world of event cameras and spiking neural networks, exploring their potential for low-power applications on SynSense's Speck chip. Discover the challenges in data, training, and deployment stages. Don't miss this talk on training robust computer vision models for neuromorphic hardware.

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Towards Training Robust Computer Vision Models for Neuromorphic Hardware

About the Speaker

Gregor Lenz graduated with a Ph.D. in neuromorphic engineering from Sorbonne University. He thinks that technology can learn a thing or two from how biological systems process information.
His main interests are event cameras that are inspired by the human retina and spiking neural networks that mimic human brain in an effort to teach machines to compute a bit more like humans do. At the very least there are some power efficiency gains to be made, but hopefully more! Also he loves to build open source software for spike-based machine learning. You can find more information on his personal website.
He is the maintainer of two open source projects in the field of neuromorphic computing, Tonic and expelliarmus .

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