Xilinx Technology Beijing Limited

Tong Meng

Xin Li

Song Han

The present disclosure proposes a fixed-point training method and apparatus based on static fixed-point conversion scheme. More specifically, the present disclosure proposes a fixed-point training method for LSTM neural network. According to this method, during the fine-tuning process of the neural network, it uses fixed-point numbers to conduct forward calculation. Accordingly, within several training cycles, the network accuracy may returned to the desired accuracy level under floating point calculation.

Fixed-point training method for deep neural networks based on static fixed-point conversion scheme

The present disclosure proposes a fixed-point training method and apparatus based on dynamic fixed-point conversion scheme. More specifically, the present disclosure proposes a fixed-point training method for LSTM neural network. According to this method, during the fine-tuning process of the neural network, it uses fixed-point numbers to conduct forward calculation. Accordingly, within several training cycles, the network accuracy may returned to the desired accuracy level under floating point calculation.

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Beijing, China

Tong Meng