Approximate Computing for Long Short Term Memory (LSTM) Neural Networks

@article{Sen2018ApproximateCF,
  title={Approximate Computing for Long Short Term Memory (LSTM) Neural Networks},
  author={Sanchari Sen and Anand Raghunathan},
  journal={IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems},
  year={2018},
  volume={37},
  pages={2266-2276}
}
Long Short Term Memory (LSTM) networks are a class of recurrent neural networks that are widely used for machine learning tasks involving sequences, including machine translation, text generation, and speech recognition. Large-scale LSTMs, which are deployed in many real-world applications, are highly compute intensive. To address this challenge, we propose AxLSTM, an application of approximate computing to improve the execution efficiency of LSTMs. An LSTM is composed of cells, each of which… CONTINUE READING

Citations

Publications citing this paper.

Optical Music Notes Recognition for Printed Music Score

  • 2018 11th International Symposium on Computational Intelligence and Design (ISCID)
  • 2018
VIEW 1 EXCERPT
CITES BACKGROUND

References

Publications referenced by this paper.
SHOWING 1-10 OF 38 REFERENCES

DeepFace: Closing the Gap to Human-Level Performance in Face Verification

  • 2014 IEEE Conference on Computer Vision and Pattern Recognition
  • 2014
VIEW 2 EXCERPTS
HIGHLY INFLUENTIAL

Approximate computing for spiking neural networks

  • Design, Automation & Test in Europe Conference & Exhibition (DATE), 2017
  • 2017
VIEW 2 EXCERPTS

FPGA-based accelerator for long short-term memory recurrent neural networks

  • 2017 22nd Asia and South Pacific Design Automation Conference (ASP-DAC)
  • 2017
VIEW 2 EXCERPTS

In-datacenter performance analysis of a tensor processing unit

  • 2017 ACM/IEEE 44th Annual International Symposium on Computer Architecture (ISCA)
  • 2017
VIEW 2 EXCERPTS

LSTM: A Search Space Odyssey

  • IEEE Transactions on Neural Networks and Learning Systems
  • 2017
VIEW 1 EXCERPT

Neural Machine Translation (Seq2Seq) Tutorial

M.-T. Luong, E. Brevdo, R. Zhao
  • 2017
VIEW 1 EXCERPT

Similar Papers

Loading similar papers…