公开(公告)号：US20250156678A1

公开(公告)日：2025-05-15

申请号：US19024363

申请日：2025-01-16

Applicant: Imagination Technologies Limited

Inventor： Daniel Barnard ,  Clifford Gibson ,  Colin McQuillan

IPC: G06N3/04 ,  G06F12/02 ,  G06F12/06 ,  G06N3/045 ,  G06N3/063

Abstract: Input data for a convolutional neural network (CNN) is stored in a buffer comprising a plurality of banks, by receiving input data comprising input data values to be processed in the CNN, determining addresses in the buffer in which the received input data values are to be stored, keeping a cursor for one or more salient positions to reduce arithmetic performed to determine the addresses in the buffer in which the received input data values are to be stored, and storing the received input data values at the determined addresses in the buffer.

公开(公告)号：US12217161B2

公开(公告)日：2025-02-04

申请号：US17498618

申请日：2021-10-11

Applicant: Imagination Technologies Limited

Inventor： Clifford Gibson ,  James Imber

IPC: G06N3/063 ,  G06F7/00 ,  G06F7/544 ,  G06N3/04 ,  G06N3/045 ,  G06N3/08

Abstract: A method of configuring a hardware implementation of a Convolutional Neural Network (CNN), the method comprising: determining, for each of a plurality of layers of the CNN, a first number format for representing weight values in the layer based upon a distribution of weight values for the layer, the first number format comprising a first integer of a first predetermined bit-length and a first exponent value that is fixed for the layer; determining, for each of a plurality of layers of the CNN, a second number format for representing data values in the layer based upon a distribution of expected data values for the layer, the second number format comprising a second integer of a second predetermined bit-length and a second exponent value that is fixed for the layer; and storing the determined number formats for use in configuring the hardware implementation of a CNN.

公开(公告)号：US20230195831A1

公开(公告)日：2023-06-22

申请号：US18096521

申请日：2023-01-12

Applicant: Imagination Technologies Limited

Inventor： Cagatay Dikici ,  Clifford Gibson ,  James Imber

IPC: G06F17/15 ,  G06N3/063

CPC classification number: G06F17/153 ,  G06N3/063

Abstract: Methods and systems for performing a convolution transpose operation between an input tensor having a plurality of input elements and a filter comprising a plurality of filter weights. The method includes: dividing the filter into a plurality of sub-filters; performing, using hardware logic, a convolution operation between the input tensor and each of the plurality of sub-filters to generate a plurality of sub-output tensors, each sub-output tensor comprising a plurality of output elements; and interleaving, using hardware logic, the output elements of the plurality of sub-output tensors to form a final output tensor for the convolution transpose.

公开(公告)号：US20190138567A1

公开(公告)日：2019-05-09

申请号：US16179270

申请日：2018-11-02

Applicant: Imagination Technologies Limited

Inventor： Chris Martin ,  David Hough ,  Clifford Gibson ,  Daniel Barnard

IPC: G06F17/15 ,  G06F7/544 ,  G06N3/04

Abstract: Hardware implementations of, and methods for processing, a convolution layer of a DNN that comprise a plurality of convolution engines wherein the input data and weights are provided to the convolution engines in an order that allows input data and weights read from memory to be used in at least two filter-window calculations performed either by the same convolution engine in successive cycles or by different convolution engines in the same cycle. For example, in some hardware implementations of a convolution layer the convolution engines are configured to process the same weights but different input data each cycle, but the input data for each convolution engine remains the same for at least two cycles so that the convolution engines use the same input data in at least two consecutive cycles.

公开(公告)号：US20190087718A1

公开(公告)日：2019-03-21

申请号：US16136553

申请日：2018-09-20

Applicant: Imagination Technologies Limited

Inventor： Chris Martin ,  David Hough ,  Paul Brasnett ,  Cagatay Dikici ,  James Imber ,  Clifford Gibson

IPC: G06N3/063 ,  G06F7/483 ,  G06N3/04

Abstract: Hardware implementations of DNNs and related methods with a variable output data format. Specifically, in the hardware implementations and methods described herein the hardware implementation is configured to perform one or more hardware passes to implement a DNN wherein during each hardware pass the hardware implementation receives input data for a particular layer, processes that input data in accordance with the particular layer (and optionally one or more subsequent layers), and outputs the processed data in a desired format based on the layer, or layers, that are processed in the particular hardware pass. In particular, when a hardware implementation receives input data to be processed, the hardware implementation also receives information indicating the desired format for the output data of the hardware pass and the hardware implementation is configured to, prior to outputting the processed data convert the output data to the desired format.

公开(公告)号：US20170323196A1

公开(公告)日：2017-11-09

申请号：US15585571

申请日：2017-05-03

Applicant: Imagination Technologies Limited

Inventor： Clifford Gibson ,  James Imber

IPC: G06N3/063 ,  G06N3/04

Abstract: A method in a hardware implementation of a Convolutional Neural Network (CNN), includes receiving a first subset of data having at least a portion of weight data and at least a portion of input data for a CNN layer and performing, using at least one convolution engine, a convolution of the first subset of data to generate a first partial result; receiving a second subset of data comprising at least a portion of weight data and at least a portion of input data for the CNN layer and performing, using the at least one convolution engine, a convolution of the second subset of data to generate a second partial result; and combining the first partial result and the second partial result to generate at least a portion of convolved data for a layer of the CNN.

公开(公告)号：US12165045B2

公开(公告)日：2024-12-10

申请号：US16136553

申请日：2018-09-20

Applicant: Imagination Technologies Limited

Inventor： Chris Martin ,  David Hough ,  Paul Brasnett ,  Cagatay Dikici ,  James Imber ,  Clifford Gibson

IPC: G06N3/065 ,  G06F1/3234 ,  G06F7/483 ,  G06F7/544 ,  G06N3/04 ,  G06N3/045 ,  G06N3/063

Abstract: Hardware implementations of DNNs and related methods with a variable output data format. Specifically, in the hardware implementations and methods described herein the hardware implementation is configured to perform one or more hardware passes to implement a DNN wherein during each hardware pass the hardware implementation receives input data for a particular layer, processes that input data in accordance with the particular layer (and optionally one or more subsequent layers), and outputs the processed data in a desired format based on the layer, or layers, that are processed in the particular hardware pass. In particular, when a hardware implementation receives input data to be processed, the hardware implementation also receives information indicating the desired format for the output data of the hardware pass and the hardware implementation is configured to, prior to outputting the processed data convert the output data to the desired format.

公开(公告)号：US20240249131A1

公开(公告)日：2024-07-25

申请号：US18623450

申请日：2024-04-01

Applicant: Imagination Technologies Limited

Inventor： Clifford Gibson ,  James Imber

IPC: G06N3/063 ,  G06F7/00 ,  G06F7/544 ,  G06N3/04 ,  G06N3/045 ,  G06N3/08

CPC classification number: G06N3/063 ,  G06F7/00 ,  G06F7/5443 ,  G06N3/04 ,  G06N3/045 ,  G06N3/08

Abstract: A method in a hardware implementation of a Convolutional Neural Network (CNN), includes receiving a first subset of data having at least a portion of weight data and at least a portion of input data for a CNN layer and performing, using at least one convolution engine, a convolution of the first subset of data to generate a first partial result; receiving a second subset of data comprising at least a portion of weight data and at least a portion of input data for the CNN layer and performing, using the at least one convolution engine, a convolution of the second subset of data to generate a second partial result; and combining the first partial result and the second partial result to generate at least a portion of convolved data for a layer of the CNN.

公开(公告)号：US11948070B2

公开(公告)日：2024-04-02

申请号：US18132929

申请日：2023-04-10

Applicant: Imagination Technologies Limited

Inventor： Clifford Gibson ,  James Imber

IPC: G06N3/063 ,  G06F7/00 ,  G06F7/544 ,  G06N3/04 ,  G06N3/045 ,  G06N3/08

CPC classification number: G06N3/063 ,  G06F7/00 ,  G06F7/5443 ,  G06N3/04 ,  G06N3/045 ,  G06N3/08

Abstract: A method in a hardware implementation of a Convolutional Neural Network (CNN), includes receiving a first subset of data having at least a portion of weight data and at least a portion of input data for a CNN layer and performing, using at least one convolution engine, a convolution of the first subset of data to generate a first partial result; receiving a second subset of data comprising at least a portion of weight data and at least a portion of input data for the CNN layer and performing, using the at least one convolution engine, a convolution of the second subset of data to generate a second partial result; and combining the first partial result and the second partial result to generate at least a portion of convolved data for a layer of the CNN.

公开(公告)号：US11868426B2

公开(公告)日：2024-01-09

申请号：US17510633

申请日：2021-10-26

Applicant: Imagination Technologies Limited

Inventor： Chris Martin ,  David Hough ,  Clifford Gibson ,  Daniel Barnard

IPC: G06F17/15 ,  G06N3/063 ,  G06F7/544 ,  G06N3/04 ,  G06N3/045

CPC classification number: G06F17/153 ,  G06F7/5443 ,  G06N3/04 ,  G06N3/063 ,  G06N3/045

Abstract: Hardware implementations of, and methods for processing, a convolution layer of a DNN that comprise a plurality of convolution engines wherein the input data and weights are provided to the convolution engines in an order that allows input data and weights read from memory to be used in at least two filter-window calculations performed either by the same convolution engine in successive cycles or by different convolution engines in the same cycle. For example, in some hardware implementations of a convolution layer the convolution engines are configured to process the same weights but different input data each cycle, but the input data for each convolution engine remains the same for at least two cycles so that the convolution engines use the same input data in at least two consecutive cycles.