公开(公告)号：US20220366255A1

公开(公告)日：2022-11-17

申请号：US17874573

申请日：2022-07-27

Applicant: Google LLC

Inventor： Jonathan Ross ,  Norman Paul Jouppi ,  Andrew Everett Phelps ,  Reginald Clifford Young ,  Thomas Norrie ,  Gregory Michael Thorson ,  Dan Luu

IPC: G06N3/08 ,  G06F15/80 ,  G06N3/063 ,  G06N5/04

Abstract: A circuit for performing neural network computations for a neural network comprising a plurality of neural network layers, the circuit comprising: a matrix computation unit configured to, for each of the plurality of neural network layers: receive a plurality of weight inputs and a plurality of activation inputs for the neural network layer, and generate a plurality of accumulated values based on the plurality of weight inputs and the plurality of activation inputs; and a vector computation unit communicatively coupled to the matrix computation unit and configured to, for each of the plurality of neural network layers: apply an activation function to each accumulated value generated by the matrix computation unit to generate a plurality of activated values for the neural network layer.

公开(公告)号：US20210019618A1

公开(公告)日：2021-01-21

申请号：US16915161

申请日：2020-06-29

Applicant: Google LLC

Inventor： Jonathan Ross ,  Norman Paul Jouppi ,  Andrew Everett Phelps ,  Reginald Clifford Young ,  Thomas Norrie ,  Gregory Michael Thorson ,  Dan Luu

IPC: G06N3/08 ,  G06F15/80 ,  G06N3/063 ,  G06N5/04

Abstract: A circuit for performing neural network computations for a neural network comprising a plurality of neural network layers, the circuit comprising: a matrix computation unit configured to, for each of the plurality of neural network layers: receive a plurality of weight inputs and a plurality of activation inputs for the neural network layer, and generate a plurality of accumulated values based on the plurality of weight inputs and the plurality of activation inputs; and a vector computation unit communicatively coupled to the matrix computation unit and configured to, for each of the plurality of neural network layers: apply an activation function to each accumulated value generated by the matrix computation unit to generate a plurality of activated values for the neural network layer.

公开(公告)号：US10896110B2

公开(公告)日：2021-01-19

申请号：US16411569

申请日：2019-05-14

Applicant: Google LLC

Inventor： Thomas Norrie ,  Naveen Kumar

IPC: G06F11/30 ,  G06F16/48 ,  G06F11/34 ,  G06F9/54 ,  G06F11/36

Abstract: A computer-implemented method executed by one or more processors, the method includes monitoring execution of program code executed by a first processor component; and monitoring execution of program code executed by a second processor component. A computing system stores data identifying hardware events in a memory buffer. The stored events occur across processor units that include at least the first and second processor components. The hardware events each include an event time stamp and metadata characterizing the event. The system generates a data structure identifying the hardware events. The data structure arranges the events in a time ordered sequence and associates events with at least the first or second processor components. The system stores the data structure in a memory bank of a host device and uses the data structure to analyze performance of the program code executed by the first or second processor components.

公开(公告)号：US20200065206A1

公开(公告)日：2020-02-27

申请号：US16665355

申请日：2019-10-28

Applicant: Google LLC

Inventor： Thomas Norrie ,  Naveen Kumar

IPC: G06F11/30 ,  G06F11/36 ,  G06F11/34 ,  G06F9/54 ,  G06F16/48

Abstract: A computer-implemented method executed by one or more processors, the method includes monitoring execution of program code executed by a first processor component; and monitoring execution of program code executed by a second processor component. A computing system stores data identifying hardware events in a memory buffer. The stored events occur across processor units that include at least the first and second processor components. The hardware events each include an event time stamp and metadata characterizing the event. The system generates a data structure identifying the hardware events. The data structure arranges the events in a time ordered sequence and associates events with at least the first or second processor components. The system stores the data structure in a memory bank of a host device and uses the data structure to analyze performance of the program code executed by the first or second processor components.

公开(公告)号：US20200019483A1

公开(公告)日：2020-01-16

申请号：US16520558

申请日：2019-07-24

Applicant: Google LLC

Inventor： Thomas Norrie ,  Naveen Kumar

IPC: G06F11/34 ,  G06F11/30 ,  G06F9/54 ,  G06F11/36

Abstract: A computer-implemented method that includes monitoring execution of program code by first and second processor components. A computing system detects that a trigger condition is satisfied by: i) identifying an operand in a portion of the program code; or ii) determining that a current time of a clock of the computing system indicates a predefined time value. The operand and the predefined time value are used to initiate trace events. When the trigger condition is satisfied the system initiates trace events that generate trace data identifying respective hardware events occurring across the computing system. The system uses the trace data to generate a correlated set of trace data. The correlated trace data indicates a time ordered sequence of the respective hardware events. The system uses the correlated set of trace data to analyze performance of the executing program code.

公开(公告)号：US20180336456A1

公开(公告)日：2018-11-22

申请号：US15983056

申请日：2018-05-17

Applicant: Google LLC

Inventor： Thomas Norrie ,  Olivier Temam ,  Andrew Everett Phelps ,  Norman Paul Jouppi

IPC: G06N3/063 ,  G06F17/16 ,  G06N3/08

CPC classification number: G06F9/3887 ,  G06F9/3001 ,  G06F9/30032 ,  G06F9/30036 ,  G06F9/30141 ,  G06F9/3885 ,  G06F15/76 ,  G06N3/00

Abstract: Methods, systems, and apparatus including a special purpose hardware chip for training neural networks are described. The special-purpose hardware chip may include a scalar processor configured to control computational operation of the special-purpose hardware chip. The chip may also include a vector processor configured to have a 2-dimensional array of vector processing units which all execute the same instruction in a single instruction, multiple-data manner and communicate with each other through load and store instructions of the vector processor. The chip may additionally include a matrix multiply unit that is coupled to the vector processor configured to multiply at least one two-dimensional matrix with a second one-dimensional vector or two-dimensional matrix in order to obtain a multiplication result.

公开(公告)号：US11586920B2

公开(公告)日：2023-02-21

申请号：US16915161

申请日：2020-06-29

Applicant: Google LLC

Inventor： Jonathan Ross ,  Norman Paul Jouppi ,  Andrew Everett Phelps ,  Reginald Clifford Young ,  Thomas Norrie ,  Gregory Michael Thorson ,  Dan Luu

IPC: G06N3/08 ,  G06F15/80 ,  G06N3/063 ,  G06N5/04

Abstract: A circuit for performing neural network computations for a neural network comprising a plurality of neural network layers, the circuit comprising: a matrix computation unit configured to, for each of the plurality of neural network layers: receive a plurality of weight inputs and a plurality of activation inputs for the neural network layer, and generate a plurality of accumulated values based on the plurality of weight inputs and the plurality of activation inputs; and a vector computation unit communicatively coupled to the matrix computation unit and configured to, for each of the plurality of neural network layers: apply an activation function to each accumulated value generated by the matrix computation unit to generate a plurality of activated values for the neural network layer.

公开(公告)号：US11275992B2

公开(公告)日：2022-03-15

申请号：US15983056

申请日：2018-05-17

Applicant: Google LLC

Inventor： Thomas Norrie ,  Olivier Temam ,  Andrew Everett Phelps ,  Norman Paul Jouppi

IPC: G06N3/08 ,  G06N3/063 ,  G06F9/30 ,  G06F9/38 ,  G06F17/16

Abstract: Methods, systems, and apparatus including a special purpose hardware chip for training neural networks are described. The special-purpose hardware chip may include a scalar processor configured to control computational operation of the special-purpose hardware chip. The chip may also include a vector processor configured to have a 2-dimensional array of vector processing units which all execute the same instruction in a single instruction, multiple-data manner and communicate with each other through load and store instructions of the vector processor. The chip may additionally include a matrix multiply unit that is coupled to the vector processor configured to multiply at least one two-dimensional matrix with a second one-dimensional vector or two-dimensional matrix in order to obtain a multiplication result.

公开(公告)号：US11182159B2

公开(公告)日：2021-11-23

申请号：US17007569

申请日：2020-08-31

Applicant: Google LLC

Inventor： Thomas Norrie ,  Gurushankar Rajamani ,  Andrew Everett Phelps ,  Matthew Leever Hedlund ,  Norman Paul Jouppi

IPC: G06F9/30 ,  G06N3/04 ,  G06F13/28

Abstract: Methods, systems, and apparatus, including computer-readable media, are described for performing vector reductions using a shared scratchpad memory of a hardware circuit having processor cores that communicate with the shared memory. For each of the processor cores, a respective vector of values is generated based on computations performed at the processor core. The shared memory receives the respective vectors of values from respective resources of the processor cores using a direct memory access (DMA) data path of the shared memory. The shared memory performs an accumulation operation on the respective vectors of values using an operator unit coupled to the shared memory. The operator unit is configured to accumulate values based on arithmetic operations encoded at the operator unit. A result vector is generated based on performing the accumulation operation using the respective vectors of values.

公开(公告)号：US10915318B2

公开(公告)日：2021-02-09

申请号：US16291176

申请日：2019-03-04

Applicant: Google LLC

Inventor： William Lacy ,  Gregory Michael Thorson ,  Christopher Aaron Clark ,  Norman Paul Jouppi ,  Thomas Norrie ,  Andrew Everett Phelps

IPC: G06F9/302 ,  G06F9/312 ,  G06F15/76 ,  G06F13/40 ,  G06F13/42 ,  G06F17/16 ,  G06N3/063 ,  G06F9/30 ,  G06F15/80 ,  G06F9/38 ,  G06F7/58 ,  G06F13/36

Abstract: A vector processing unit is described, and includes processor units that each include multiple processing resources. The processor units are each configured to perform arithmetic operations associated with vectorized computations. The vector processing unit includes a vector memory in data communication with each of the processor units and their respective processing resources. The vector memory includes memory banks configured to store data used by each of the processor units to perform the arithmetic operations. The processor units and the vector memory are tightly coupled within an area of the vector processing unit such that data communications are exchanged at a high bandwidth based on the placement of respective processor units relative to one another, and based on the placement of the vector memory relative to each processor unit.