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GTC ON-DEMAND

Artificial Intelligence and Deep Learning
Presentation
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Optimizing Training Performance of Recurrent Neural Networks
Abstract:
By exposing parallelism between operations in a recurrent neural network it is possible to achieve significant performance improvements when training. In this talk a case study based on a Long Short-Term Memory (LSTM) recurrent network will be used to demonstrate a 5x speedup over a naive implementation for the forward pass of a single layer. A further 2x speedup (totaling 10x) will be shown when considering multiple layers. Results will also be presented for the backward pass.
 
Topics:
Artificial Intelligence and Deep Learning, Performance Optimization
Type:
Talk
Event:
GTC Silicon Valley
Year:
2016
Session ID:
S6165
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