公开(公告)号：US11663814B2

公开(公告)日：2023-05-30

申请号：US16855681

申请日：2020-04-22

Applicant: Arm Limited

Inventor： Urmish Ajit Thakker ,  Jin Tao ,  Ganesh Suryanarayan Dasika ,  Jesse Garrett Beu

IPC: G06N3/08 ,  G06N3/082 ,  G06F17/18 ,  G06K9/62 ,  G06N3/04

CPC classification number: G06N3/082 ,  G06F17/18 ,  G06K9/6267 ,  G06N3/0472

Abstract: The present disclosure advantageously provides a system and a method for skipping recurrent neural network (RNN) state updates using a skip predictor. Sequential input data are received and divided into sequences of input data values, each input data value being associated with a different time step for a pre-trained RNN model. At each time step, the hidden state vector for a prior time step is received from the pre-trained RNN model, and a determination, based on the input data value and the hidden state vector for at least one prior time step, is made whether to provide or not provide the input data value associated with the time step to the pre-trained RNN model for processing. When the input data value is not provided, the pre-trained RNN model does not update its hidden state vector. Importantly, the skip predictor is trained without retraining the pre-trained RNN model.

公开(公告)号：US20210056422A1

公开(公告)日：2021-02-25

申请号：US16855681

申请日：2020-04-22

Applicant: Arm Limited

Inventor： Urmish Ajit Thakker ,  Jin Tao ,  Ganesh Suryanarayan Dasika ,  Jesse Garrett Beu

IPC: G06N3/08 ,  G06N3/04 ,  G06K9/62 ,  G06F17/18

Abstract: The present disclosure advantageously provides a system and a method for skipping recurrent neural network (RNN) state updates using a skip predictor. Sequential input data are received and divided into sequences of input data values, each input data value being associated with a different time step for a pre-trained RNN model. At each time step, the hidden state vector for a prior time step is received from the pre-trained RNN model, and a determination, based on the input data value and the hidden state vector for at least one prior time step, is made whether to provide or not provide the input data value associated with the time step to the pre-trained RNN model for processing. When the input data value is not provided, the pre-trained RNN model does not update its hidden state vector. Importantly, the skip predictor is trained without retraining the pre-trained RNN model.