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2019

Measuring scheduling efficiency of RNNs for NLP applications

Thakker, Urmish, Dasika, Ganesh, Beu, Jesse et al.

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Recurrent neural networks (RNNs) have shown state of the art results for speech recognition, natural language processing, image captioning and video summarizing applications.

Many of these applications run on low-power platforms, so their energy efficiency is extremely important.
We observed that cache-oblivious RNN scheduling during inference typically results in 30-50x more data transferred on and off the CPU than the application's working set size.
This can potentially impact its energy efficiency.

Measuring scheduling efficiency of RNNs for NLP applications · Around

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G. Eason, B. Noble, and I. N. Sneddon, “On certain integrals of Lipschitz-Hankel type involving products of Bessel functions,” Phil. Trans. Roy. Soc. London, vol. A247, pp. 529–551, April 1955
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S Hochreiter, J Schmidhuber, “Long Short-Term Memory,” Neural Computation, 9(8):1735–1780, 1997. URL https://doi.org/10.1162/neco.1997.9.8.1735
1997
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K Cho, B Merrienboer, C Gehre, F Bougares, H Schwenk, H Schwenk, Y Bengio “Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation” CoRR, abs/1406.1078, 2014. URL http://arxiv.org/abs/1406.1078
Original
2014
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A Hannun, C Case, J Casper, B Catanzaro, G Diamos, E Elsen, R Pregner, S Satheesh, S Sengupta, A Coates, A Ng, “Deep Speech: Scaling up end-to-end speech recognition” CoRR, abs/1412.5567, 2014. URL http://arxiv.org/abs/1412.5567
Original
2014
Cited alongside, same era.
Y Wu, M Schuster, Z Chen, Q Le, M Norouzi, W Macherey, J Dean et al, “Google’s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation”
Cited in the paper.

Then

Dan G, Cliff B, Oriol V, Amarnag S “Multilingual Language Processing From Bytes” CoRR, abs/1609.08144, 2016. URL http://arxiv.org/abs/1609.08144
Original
2016
Later among the works it cites.
R Jozefowicz, O Vinyals, M Schuster, N Shazeer, Y Wu “Exploring the Limits of Language Modeling” CoRR, abs/1602.02410, 2016. URL http://arxiv.org/abs/1602.02410
Original
2016
Later among the works it cites.

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