QUANTIZING TRAINED LONG SHORT-TERM MEMORY NEURAL NETWORKS

Method for quantizing a trained long short-term memory (LSTM) neural network having a plurality of weights, the method comprising: obtaining data specifying trained floating-point values for each of the weights of the trained LSTM neural network, the trained LSTM neural network comprising one or more LSTM layers, each LSTM layer having a plurality of gates and each of the plurality of gates being associated with an input weight matrix and a recurrent weight matrix; quantizing the trained LSTM neural network, comprising: for each gate, quantizing the elements of the input weight matrix to a target fixed bit-width; for each gate, quantizing the elements of the recurrent weight matrix to the target fixed bit-width; and providing data specifying a quantized LSTM neural network for use in performing quantized inference.