FIXED-POINT TRAINING METHOD FOR DEEP NEURAL NETWORKS BASED ON DYNAMIC FIXED-POINT CONVERSION SCHEME

摘要

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.