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2019 GTC San Jose
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S9151 - Training Spiking Neural Networks on GPUs with Bidirectional Interleaved Complementary Hierarchical Networks

Session Speakers
Session Description

We will discuss using spiking neural networks on GPUs to train audio and vision networks with our new BICHNN architecture. Spiking neural networks are more brain-like and have richer time-domain signal processing behavior than traditional feed-forward networks, but back propagation and gradient descent don't work on spiking neural nets. We'll describe how we train our models using our Bidirectional Interleaved Hierarchical Neural Networks. We will show how we construct these spiking neural networks in our NeuroCAD visual design tool, then connect them up with parameter-driven probability maps to give us the BICHNN architecture. Then we will turn them loose and train them in real-time and demonstrate how we use genetic algorithms and massive amounts of GPU simulation time to optimize the networks to the specified task.


Additional Information
Advanced AI Learning Techniques (incl. GANs/NTMs)
Advanced AI Learning Techniques (Incl. GANs/NTMs), Deep Learning/AI Frameworks
General, Software
Intermediate technical
Talk
50 minutes
Session Schedule