ROTATING DATA FOR NEURAL NETWORK COMPUTATIONS

• US 20160342893A1
• Filed: 09/03/2015
• Published: 11/24/2016
• Est. Priority Date: 05/21/2015
• Status: Active Grant

• Current Assignee: Google LLC (Alphabet Inc.)
• Original Assignee: Google Inc. (Alphabet Inc.)
• Inventors: Ross, Jonathan, Thorson, Gregory Michael
• Granted Patent: US 9,805,303 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 15/8046 Systolic arrays
• CPC Class Codes: G06F 17/153 Multidimensional correlation or convolution
• CPC Class Codes: G06N 3/045 Combinations of networks
• CPC Class Codes: G06N 3/063 using electronic means
• CPC Class Codes: G06N 3/08 Learning methods
• CPC Class Codes: G06N 5/04 Inference or reasoning models

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.

Claims

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, when executed by one or more computers, cause the one or more computers to perform operations 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 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.

16. The computer-readable medium of claim 15, 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.

17. The computer-readable medium of claim 16, 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.

18. The computer-readable medium of claim 16, 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.

19. The computer-readable medium of claim 15, 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.

20. The computer-readable medium of claim 15, 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.

21. The computer-readable medium of claim 15, 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.