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C-LSTM: Enabling Efficient LSTM using Structured Compression Techniques on FPGAs
[article]
2018
arXiv
pre-print
Recently, significant accuracy improvement has been achieved for acoustic recognition systems by increasing the model size of Long Short-Term Memory (LSTM) networks. Unfortunately, the ever-increasing size of LSTM model leads to inefficient designs on FPGAs due to the limited on-chip resources. The previous work proposes to use a pruning based compression technique to reduce the model size and thus speedups the inference on FPGAs. However, the random nature of the pruning technique transforms
arXiv:1803.06305v1
fatcat:st6r57l6grbu3jprxtqo7bo2q4