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Long Short-Term Memory (LSTM) is widely used in speech recognition.
Darpa timit acoustic-phonetic continous speech corpus cd-rom. nist speech disc 1-1.1
J. S. Garofolo, L. F. Lamel, W. M. Fisher, J. G. Fiscus, and D. S. Pallett · 1993
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Long short-term memory
S. Hochreiter and J. Schmidhuber · 1997
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Sparse matrix-vector multiplication on fpgas
L. Zhuo and V. K. Prasanna · 2005
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An Overview of Modern Speech Recognition
L. D. Xuedong Huang · 2010
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The Kaldi speech recognition toolkit
D. Povey, A. Ghoshal, G. Boulianne, L. Burget, O. Glembek, N. Goel, M. Hannemann, P. Motlicek, Y. Qian, P. Schwarz, et al · 2011
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Diannao: a small-footprint high-throughput accelerator for ubiquitous machine-learning
T. Chen, Z. Du, N. Sun, J. Wang, C. Wu, Y. Chen, and O. Temam · 2014
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Dadiannao: A machine-learning supercomputer
Y. Chen, T. Luo, S. Liu, S. Zhang, L. He, J. Wang, L. Li, T. Chen, Z. Xu, N. Sun, and O. Temam · 2014
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A scalable sparse matrix-vector multiplication kernel for energy-efficient sparse-blas on FPGAs
R. Dorrance, F. Ren, et al · 2014
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A high memory bandwidth fpga accelerator for sparse matrixvector multiplication
J. Fowers, K. Ovtcharov, K. Strauss, et al · 2014
Cited alongside, same era.
Deep speech: Scaling up end-to-end speech recognition
A. Hannun, C. Case, J. Casper, B. Catanzaro, G. Diamos, E. Elsen, R. Prenger, S. Satheesh, S. Sengupta, A. Coates, and A. Ng · 2014
Cited alongside, same era.
Long short-term memory recurrent neural network architectures for large scale acoustic modeling
H. Sak et al · 2014
Cited alongside, same era.
Recurrent neural networks hardware implementation on FPGA
A. X. M. Chang, B. Martini, and E. Culurciello · 2015
Cited alongside, same era.
Learning both weights and connections for efficient neural networks
S. Han, J. Pool, J. Tran, and W. J. Dally · 2015
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Angel-eye: A complete design flow for mapping cnn onto customized hardware
K. Guo, L. Sui, et al · 2016
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Eie: efficient inference engine on compressed deep neural network
S. Han, X. Liu, H. Mao, J. Pu, A. Pedram, M. A. Horowitz, and W. J. Dally · 2016
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Deep Compression: Compressing deep neural networks with pruning, trained quantization and huffman coding
S. Han, H. Mao, and W. J. Dally · 2016
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Fpga-based low-power speech recognition with recurrent neural networks
M. Lee, K. Hwang, J. Park, S. Choi, S. Shin, and W. Sung · 2016
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Z. Du, R. Fasthuber, T. Chen, P. Ienne, L. Li, T. Luo, X. Feng, Y. Chen, and O. Temam · 2015
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Deep speech 2: End-to-end speech recognition in english and mandarin
D. A. et al · 2015
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