FAST COMPUTATION OF A CONVOLUTIONAL NEURAL NETWORK

• US 20200134400A1
• Filed: 04/22/2019
• Published: 04/30/2020
• Est. Priority Date: 10/24/2018
• Status: Active Grant

• Current Assignee: Advanced New Technologies Company Limited (Ant Group Co., Ltd.)
• Original Assignee: Alibaba Group Holding Ltd.
• Inventors: Liu, Yongchao, Huang, Qiyin, Pan, Guozhen, Li, Sizhong, Xu, Jianguo, Zhang, Haitao, Wang, Lin
• Granted Patent: US 10,635,951 B1
• Time in Patent Office: Days
• CPC Class Codes: G06N 3/04 Architecture, e.g. interconnection topology
• CPC Class Codes: G06N 3/045 Combinations of networks
• CPC Class Codes: G06N 3/08 Learning methods
• CPC Class Codes: G06V 10/454 Integrating the filters into a hierarchical structure, e.g. convolutional neural networks [CNN]
• CPC Class Codes: G06V 10/82 using neural networks
• CPC Class Codes: G06V 30/19173 Classification techniques
• CPC Class Codes: G06V 40/172 Classification, e.g. identification

Abstract

A computer-implemented method includes obtaining a trained convolutional neural network comprising one or more convolutional layers, each of the one or more convolutional layers comprising a plurality of filters with known filter parameters; pre-computing a reusable factor for each of the one or more convolutional layers based on the known filter parameters of the trained convolutional neural network; receiving input data to the trained convolutional neural network; computing an output of the each of the one or more convolutional layers using a Winograd convolutional operator based on the pre-computed reusable factor and the input data; and determining output data of the trained convolutional network based on the output of the each of the one or more convolutional layers.

Claims

1. A computer-implemented method, comprising

obtaining, by a data processing apparatus, a trained convolutional neural network comprising at least a first convolutional layer and a second convolutional layer, each of the at least the first convolutional layer and the second convolutional layer comprising a corresponding plurality of filters with known filter parameters;

pre-computing, by the data processing apparatus, a first reusable factor for the first convolutional layer based on the known filter parameters regardless of any input data to the trained convolutional neural network;

pre-computing, by the data processing apparatus, a second reusable factor for the second convolutional layer based on the known filter parameters regardless of any input data to the trained convolutional neural network;

identifying, by the data processing apparatus, a first input data to the first convolutional layer of the trained convolutional neural network;

computing, by the data processing apparatus, a first output of the first convolutional layer using a Winograd convolutional operator based on the first reusable factor and the first input data;

using, by the data processing apparatus, the first output as a second input data to the second convolutional layer of the trained convolutional neural network; and

computing, by the data processing apparatus, a second output of the second convolutional layer using the Winograd convolutional operator based on the second reusable factor and the second input data.

3. The computer-implemented method of claim 1, further comprising saving the first reusable factor for the first convolutional layer.

4. The computer-implemented method of claim 1, wherein computing a first output of the first convolutional layer using a Winograd convolutional operator based on the first reusable factor and the first input data comprises computing the first output of the first convolutional layer according to a Winograd minimal filtering algorithm with the first reusable factor and the first input data

5. The computer-implemented method of claim 1, wherein the first input data to the trained convolutional neural network comprises one or more images.

6. The computer-implemented method of claim 1, further comprising:

determining, by the data processing apparatus, output data of the trained convolutional neural network based on the second output of the second convolutional layer.

7. The computer-implemented method of claim 1, further comprising outputting output data of the trained convolutional neural network via a user interface

2. (canceled)

8. A non-transitory, computer-readable medium storing one or more instructions executable by a computer system to perform operations comprising:

obtaining a trained convolutional neural network comprising at least a first convolutional laver and a second convolutional layer, each of the at least the first convolutional laver and the second convolutional layer comprising a corresponding plurality of filters with known filter parameters;

pre-computing a first reusable factor for the first convolutional layer based on the known filter parameters regardless of any input data to the trained convolutional neural network;

pre-computing a second reusable factor for the second convolutional layer based on the known filter parameters regardless of any input data to the trained convolutional neural network;

identifying a first input data to the first convolutional laver of the trained convolutional neural network;

computing a first output of the first convolutional layer using a Winograd convolutional operator based on the first reusable factor and the first input data;

using the first output as a second input data to the second convolutional layer of the trained convolutional neural network; and

computing a second output of the second convolutional laver using the Winograd convolutional operator based on the second reusable factor and the second input data.

10. The non-transitory, computer-readable medium of claim 8, the operations further comprising saving the first reusable factor for the first convolutional layer.

11. The non-transitory, computer-readable medium of claim 8, wherein computing a first output of the first convolutional layer using a Winograd convolutional operator based on the first reusable factor and the first input data comprises computing the first output of the first convolutional layer according to a Winograd minimal filtering algorithm with the first reusable factor and the first input data.

12. The non-transitory, computer-readable medium of claim 8, wherein the first input data to the trained convolutional neural network comprises one or more images.

13. The non-transitory, computer-readable medium of claim 8, the operations further comprising:

determining output data of the trained convolutional neural network based on the second output of the second convolutional layer.

14. The non-transitory, computer-readable medium of claim 8, the operations further comprising outputting output data of the trained convolutional neural network via a user interface.

9. (canceled)

15. A computer-implemented system, comprising:

one or more computers; and

one or more computer memory devices interoperably coupled with the one or more computers and having tangible, non-transitory, machine-readable media storing instructions, that when executed by the one or more computers, perform operations comprising;

obtaining a trained convolutional neural network comprising at least a first convolutional layer and a second convolutional layer, each of the at least the first convolutional layer and the second convolutional layer comprising a corresponding plurality of filters with known filter parameters.

pre-computing a first reusable factor for the first convolutional layer based on the known filter parameters regardless of any input data to the trained convolutional neural network;

pre-computing a second reusable factor for the second convolutional layer based on the known filter parameters regardless of any input data to the trained convolutional neural network;

identifying a first input data to the first convolutional laver of the trained convolutional neural network;

computing a first output of the first convolutional layer using a Winograd convolutional operator based on the first reusable factor and the first input data;

using the first output as a second input data to the second convolutional layer of the trained convolutional neural network; and

computing a second output of the second convolutional laver using the Winograd convolutional operator based on the second reusable factor and the second input data.

17. The computer-implemented system of claim 15, the operations further comprising saving the first reusable factor for the first convolutional layer.

18. The computer-implemented system of claim 15, wherein computing a first output of the first convolutional layer using a Winograd convolutional operator based on the first reusable factor and the first input data comprises computing the first output of the first convolutional layer according to a Winograd minimal filtering algorithm with the first reusable factor and the first input data.

19. The computer-implemented system of claim 15, the operations further comprising:

determining output data of the trained convolutional neural network based on the second output of the second convolutional layer.

20. The computer-implemented system of claim 15, further comprising a user interface;

and the operations further comprising outputting output data of the trained convolutional neural network via the user interface.

16. (canceled)