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GTC ON-DEMAND

Artificial Intelligence and Deep Learning
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High Performance CTC Training for End-to-End Speech Recognition on GPU
Abstract:
End-to-end speech recognition systems, which directly transcribe audio data with text without requiring an intermediate phonetic representation, are based on recurrent neural network (RNN) + connectionist temporal classification (CTC). CTC is to automatically learn the alignments between speech frames and the label sequence of transcript. In this work, we focus on optimizing CTC training, especially the forward-backward algorithm, on GPU. Firstly, opportunities of saving computation and memory access of CTC forward-backward algorithm were quantitatively analyzed and utilized to get a speedup of ~1.28X. Secondly, by data reuse among frames and data transfer between frames through register file and shared memory, we get a speedup of ~1.80X.
 
Topics:
Artificial Intelligence and Deep Learning
Type:
Talk
Event:
GTC Silicon Valley
Year:
2016
Session ID:
S6383
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