Cascaded computing for convolutional neural networks

Patent metadata

Field	Value
Publication number	US-11775831-B2
Application number	US-202318154727-A
Country	US
Kind code	B2
Filing date	Jan 13, 2023
Priority date	Sep 26, 2016
Publication date	Oct 3, 2023
Grant date	Oct 3, 2023

Abstract

Techniques are described for efficiently reducing the amount of total computation in convolutional neural networks (CNNs) without affecting the output result or classification accuracy. Computation redundancy in CNNs is reduced by exploiting the computing nature of the convolution and subsequent pooling (e.g., sub-sampling) operations. In some implementations, the input features may be divided into a group of precision values and the operation(s) may be cascaded. A maximum may be identified (e.g., by 90% probability) using a small number of bits in the input features, and the full-precision convolution may then be performed on the maximum input. Accordingly, the total number of bits used to perform the convolution is reduced without affecting the output features or the final classification accuracy.

First claim

What is claimed is:

1. One or more non-transitory computer-readable storage media storing instructions which, when executed by at least one processor, cause the at least one processor to perform operations comprising:
in one or more layers of a convolutional neural network (CNN), performing a first iteration that includes computing a value based on a first set of most significant bits (MSBs) for each of a plurality of data sets;
examining a first set of values computed for the plurality of data sets in the first iteration to determine whether a maximum value is present among the first set of values;
responsive to identifying the maximum value, performing a full precision computation of the value for a data set, of the plurality of data sets, that exhibited the maximum value; and
propagating the full precision computation of the value to a subsequent layer of the CNN.

2. The one or more non-transitory computer-readable storage media of claim 1 , further comprising:
responsive to determining that the first set of values are the same, performing, by the at least one processor, a second iteration that includes computing the value based on a second set of MSBs for each of the plurality of data sets, the second set of MSBs being larger than the first set of MSBs.

3. The one or more non-transitory computer-readable storage media of claim 2 , further storing instructions, which, when executed by the at least one processor, cause the at least one processor to perform operations comprising:
examining a second set of values computed for the plurality of data sets in the second iteration to determine whether the maximum value is present among the second set of values; and
responsive to identifying the maximum value among the second set of values, performing, by the at least one processor, the full precision computation of the value for a data set, of the plurality of data sets, that exhibited the maximum value in the second iteration.

4. The one or more non-transitory computer-readable storage media of claim 2 , wherein the computing in each of the first iteration and the second iteration employs a convolution and a pooling.

5. The one or more non-transitory computer-readable storage media of claim 4 , wherein the convolution is a N×N convolution, where N is any integer.

6. The one or more non-transitory computer-readable storage media of claim 4 , wherein the pooling is a N×N pooling, where N is any integer.

7. The one or more non-transitory computer-readable storage media of claim 4 , wherein the convolution is a 3×3 convolution, and the pooling is a 2×2 pooling.

8. The one or more non-transitory computer-readable storage media of claim 2 , wherein at least one of the first iteration and the second iteration is performed with a precision less than that of the full precision computation.

9. The one or more non-transitory computer-readable storage media of claim 8 , wherein the precision is 8-bit precision.

10. The one or more non-transitory computer-readable storage media of claim 1 , wherein the CNN is employed to analyze an image.

11. The one or more non-transitory computer-readable storage media of claim 1 , wherein:
the first iteration computes a value that approximates the full precision computation of the value; and
the full precision computation is performed on the data set that includes less data than the plurality of data sets.

Assignees

• Seo Jae Sun
• Kim Minkyu
• Univ Arizona State

Inventors

• Seo Jae-Sun
• Kim Minkyu

Classifications

• G06N3/0464 Primary

  Convolutional networks [CNN, ConvNet] · CPC title

• G06N3/08 Primary

  Learning methods · CPC title

• G06F7/48

  using non-contact-making devices, e.g. tube, solid state device; using unspecified devices · CPC title

• G06N3/045 Primary

  Combinations of networks · CPC title