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2017 GTC San Jose

S7580 - ZipML: Faster Machine Learning via Low-Precision Communication and Computation

Session Speakers
Session Description

We'll present new techniques for training machine learning models using low-precision computation and communication. We'll start by briefly outlining new theoretical results proving that, surprisingly, many fundamental machine learning tools, such as dense generalized linear models, can be trained end-to-end (samples, model, and gradients) using low precision (as little as one bit per value), while still guaranteeing convergence. We'll then explore the implications of these techniques with respect to two key practical applications: multi-GPU training of deep neural networks, and compressed sensing for medical and astronomical data.


Additional Session Information
Intermediate
Talk
Deep Learning and AI Performance Optimization
Higher Education / Research
50 minutes
Session Schedule