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2017 GTC San Jose

S7265 - Quasi-Recurrent Neural Networks - A Highly Optimized RNN Architecture for the GPU

Session Speakers
Session Description

We introduce quasi-recurrent neural networks (QRNNs), an approach to neural sequence modeling that provides predictive accuracy equal or better than cuDNN LSTMs while being up to 16 times faster at train and test time than the highly optimized NVIDIA cuDNN LSTM implementation. This is possible by constructing an RNN architecture tailored to achieve high throughput on an NVIDIA GPU using convolutional layers, which apply in parallel across timesteps, and a minimalist recurrent pooling function written in CUDA, which applies in parallel across channels. We'll discuss in detail the design choices of the QRNN, including how to investigate GPU efficiency using the NVIDIA Visual Profiler, and finally our experiments on language modeling, sentiment classification, and character-level neural machine translation that show the advantages and viability of QRNNs as a basic building block for a variety of sequence tasks.


Additional Session Information
Intermediate
Talk
Deep Learning and AI Performance Optimization Tools and Libraries
Retail / Etail
50 minutes
Session Schedule