ESE: Efficient Speech Recognition Engine with Sparse LSTM on FPGA

@inproceedings{Han2016ESEES,
  title={ESE: Efficient Speech Recognition Engine with Sparse LSTM on FPGA},
  author={Song Han and Junlong Kang and Huizi Mao and Yiming Hu and Xin Li and Yubin Li and Dongliang Xie and Hong Luo and Song Yao and Yu Wang and Huazhong Yang and William J. Dally},
  booktitle={FPGA},
  year={2016}
}
Long Short-Term Memory (LSTM) is widely used in speech recognition. In order to achieve higher prediction accuracy, machine learning scientists have built increasingly larger models. Such large model is both computation intensive and memory intensive. Deploying such bulky model results in high power consumption and leads to a high total cost of ownership (TCO) of a data center. To speedup the prediction and make it energy efficient, we first propose a load-balance-aware pruning method that can… CONTINUE READING

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