Rotating data for neural network computations

Patent metadata

Field	Value
Publication number	US-2016342893-A1
Application number	US-201514845022-A
Country	US
Kind code	A1
Filing date	Sep 3, 2015
Priority date	May 21, 2015
Publication date	Nov 24, 2016
Grant date	—

Abstract

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for computing a layer output for a convolutional neural network layer, the method comprising: receiving a plurality of activation inputs; forming a plurality of vector inputs from the plurality of activation inputs, each vector input comprising values from a distinct region within the multi-dimensional matrix; sending the plurality of vector inputs to one or more cells along a first dimension of the systolic array; generating a plurality of rotated kernel structures from each of the plurality of kernel; sending each kernel structure and each rotated kernel structure to one or more cells along a second dimension of the systolic array; causing the systolic array to generate an accumulated output based on the plurality of value inputs and the plurality of kernels; and generating the layer output from the accumulated output.

First claim

What is claimed is:

1 . A method for computing a layer output for a convolutional neural network layer from a layer input for the convolutional neural network layer using a two-dimensional systolic array, the convolutional neural network layer having a plurality of kernels, each kernel having a respective matrix structure of weights, the method comprising:
receiving a plurality of activation inputs, the plurality of activation inputs represented as a multi-dimensional matrix; forming a plurality of vector inputs from the plurality of activation inputs, each vector input comprising values from a distinct region within the multi-dimensional matrix; sending the plurality of vector inputs to one or more cells along a first dimension of the systolic array; generating a plurality of rotated kernel structures from each of the plurality of kernels, where generating a particular rotated kernel structure comprises shifting elements in the respective matrix structure for the kernel along one dimension; sending each kernel structure and each rotated kernel structure to one or more cells along a second dimension of the systolic array; causing the systolic array to generate an accumulated output based on the plurality of value inputs and the plurality of kernels; and generating the layer output from the accumulated output.

2 . The method of claim 1 , where the first dimension of the systolic array corresponds to rows of the systolic array, and where the second dimension of the systolic array corresponds to columns of the systolic array.

3 . The method of claim 2 , where sending the plurality of vector inputs to one or more cells comprises:
sending, for a particular row of the systolic array, a respective element from each vector input to the particular row; and selecting, at each cell in the particular row, one of the respective elements for use in a register in the cell based on a multiplexor control signal.

4 . The method of claim 2 , where sending the plurality of vector inputs to one or more cells along a first dimension of the systolic array comprises:
sending each vector input to a distinct series of shift registers, each shift register shifting an element of the vector input to a subsequent shift register on a subsequent clock cycle, each shift register corresponding to a respective row in the systolic array; and selecting, for each row, an output from the corresponding shift registers for use in the TOW.

5 . The method of claim 1 , where forming a plurality of vector inputs from the plurality of activation inputs is based on a size of a particular kernel structure, further comprising:
overlapping the particular kernel structure with the matrix representation of the plurality of activation inputs to form a first vector input from elements in the matrix representation; forming one or more other vector inputs from other elements that surround the overlapped particular kernel structure.

6 . The method of claim 1 , where generating the layer output from the accumulated output comprises normalizing the accumulated output, pooling the accumulated output, or both, to generate the layer output.

7 . The method of claim 1 , where sending the plurality of vector inputs to one or more cells along a first dimension of the systolic array comprises:
at a particular clock cycle, storing a first vector input in the plurality of vector inputs in a first cell of the systolic array; and at a subsequent clock cycle, shifting the first vector input in the first cell to a second cell that is adjacent to the first cell and storing a second vector input in the plurality of vector inputs in the first cell.

8 . A system for computing a layer output for a convolutional neural network layer from a layer input for the convolutional neural network layer using a two-dimensional systolic array, the convolutional neural network layer having a plurality of kernels, each kernel having a respective matrix structure of weights, the system comprising:
one or more computers; and computer-readable medium coupled to the one or more computers and having instructions stored thereon, which, when executed by the one or more computers, cause the one or more computers to perform operations comprising: receiving a plurality of activation inputs, the plurality of activation inputs represented as a multi-dimensional matrix; forming a plurality of vector inputs from the plurality of activation inputs, each vector input comprising values from a distinct region within the multi-dimensional matrix; sending the plurality of vector inputs to one or more cells along a first dimension of the systolic array; generating a plurality of rotated kernel structures from each of the plurality of kernels, where generating a particular rotated kernel structure comprises shifting elements in the respective matrix structure for the kernel along one dimension; sending each kernel structure and each rotated kernel structure to one or more cells along a second dimension of the systolic array; causing the systolic array to generate an accumulated output based on the plurality of value inputs and the plurality of kernels; and generating the layer output from the accumulated output.

9 . The system of claim 8 , where the first dimension of the systolic array corresponds to rows of the systolic array, and where the second dimension of the systolic array corresponds to columns of the systolic array.

10 . The system of claim 9 , where sending the plurality of vector inputs to one or more cells comprises:
sending, for a particular row of the systolic array, a respective element from each vector input to the particular row; and selecting, at each cell in the particular row, one of the respective elements for use in a register in the cell based on a multiplexor control signal.

11 . The system of claim 9 , where sending the plurality of vector inputs to one or more cells along a first dimension of the systolic array comprises:
sending each vector input to a distinct series of shift registers, each shift register shifting an element of the vector input to a subsequent shift register on a subsequent clock cycle, each shift register corresponding to a respective row in the systolic array; and selecting, for each row, an output from the corresponding shift registers for use in the TOW.

12 . The system of claim 8 , where forming a plurality of vector inputs from the plurality of activation inputs is based on a size of a particular kernel structure, further comprising:
overlapping the particular kernel structure with the matrix representation of the plurality of activation inputs to form a first vector input from elements in the matrix representation; forming one or more other vector inputs from other elements that surround the overlapped particular kernel structure.

13 . The system of claim 8 , where generating the layer output from the accumulated output comprises normalizing the accumulated output, pooling the accumulated output, or both, to generate the layer output.

14 . The system of claim 8 , where sending the plurality of vector inputs to one or more cells along a first dimension of the systolic array comprises:
at a particular clock cycle, storing a first vector input in the plurality of vector inputs in a first cell of the systolic array; and at a subsequent clock cycle, shifting the first vector input in the first cell to a second cell that is adjacent to the first cell and storing a second vector input in the plurality of vector inputs in the first cell.

15 . A computer-readable medium having instructions stored thereon, which, wh

Assignees

• Google Inc

Inventors

• Ross Jonathan
• Thorson Gregory Michael

Classifications

• G06N3/045

  Combinations of networks · CPC title

• G06N3/08 Primary

  Learning methods · CPC title

• G06N3/0464

  Convolutional networks [CNN, ConvNet] · CPC title

• G06N3/063 Primary

  using electronic means · CPC title

• G06F17/153

  Multidimensional correlation or convolution · CPC title