Schedule
Timezone: PDT
Always-on visual classification below 1 mW with spiking convolutional networks on Dynap™-CNN
Martino SORBARO, R&D Scientist
SynSense
Neuromorphic hardware enables real-world applications in computer vision, audition and other sensory modalities to be deployed with very low power consumption (<1 mW). Commercial neuromorphic solutions are beginning to emerge, based on inference in spiking neural networks. In these systems, computation is performed using asynchronous 1-bit binary signals, and sensory input is processed in real-time. In this talk we will present our approach to training spiking convolutional neural networks for practical applications, and showcase our results on real-world data, presenting our novel Dynap™-CNN convolutional neuromorphic chip. We will illustrate the pipeline from data collection to training, simulation and on-chip classification of visual scenes at an average power consumption below 1 mW.
Martino SORBARO, R&D Scientist
SynSense
Martino Sorbaro is a research and development scientist at SynSense AG, Zürich, Switzerland, and a postdoc at the institute of Neuroinformatics of the University of Zürich and ETH. He obtained a MSc in physics at the university of Pavia, Italy, and a PhD in neuroinformatics at the university of Edinburgh, Scotland. His current work focuses on learning in spiking neural networks, both for theoretical research and technological applications.
Schedule subject to change without notice.