公开(公告)号：US20230031537A1

公开(公告)日：2023-02-02

申请号：US17852450

申请日：2022-06-29

申请人： Imagination Technologies Limited

发明人： Biswarup Choudhury ,  Cagatay Dikici ,  Jason Rogers ,  Pedro Silva

IPC分类号： G06N3/063 ,  G06N3/04

摘要： A method of implementing in hardware a bidirectional recurrent neural network (BRNN) for operation on a sequence of inputs, each step of the BRNN being for operation on (a) an input of the sequence, (b) corresponding backward state generated in respect of a subsequent input of the sequence, and (c) corresponding forward state generated in respect of a preceding input of the sequence. A representation of the BRNN is transformed into a derivative neural network equivalent to the BRNN over the sequence of inputs. The derivative neural network includes a forward recurrent neural network (RNN) for operation on the forward state over the inputs of the sequence, and a backward recurrent neural network (RNN) for operation on the backward state over the inputs of the sequence. The derivative neural network is implemented in hardware so as to perform the BRNN on the sequence of inputs.

公开(公告)号：US20230068394A1

公开(公告)日：2023-03-02

申请号：US17852964

申请日：2022-06-29

申请人： Imagination Technologies Limited

发明人： Biswarup Choudhury ,  Cagatay Dikici ,  Jason Rogers ,  Pedro Silva

IPC分类号： G06N3/063 ,  G06N3/04

摘要： A computer-implemented method of selecting a number format for use in configuring a hardware implementation of a bidirectional recurrent neural network (BRNN) for operation on a sequence of inputs. A received BRNN representation is implemented as a test neural network equivalent to the BRNN over a sequence of input tensors, each step of the test neural network being for operation on (a) an input tensor of the sequence, (b) a corresponding backward state tensor generated in respect of a subsequent input tensor of the sequence, and (c) a corresponding forward state tensor generated in respect of a preceding input tensor of the sequence. The test neural network includes a forward recurrent neural network (RNN) for operation on the forward state tensors over the input tensors of the sequence; and a backward RNN for operation on the backward state tensors over the input tensors of the sequence. A number format selection algorithm is applied to collected operating statistics so as to derive a common number format for a plurality of instances of one or more selected tensors of the test neural network.