Dynamic quantization for deep neural network inference system and method

Patent metadata

Field	Value
Publication number	US-11580719-B2
Application number	US-202017128365-A
Country	US
Kind code	B2
Filing date	Dec 21, 2020
Priority date	Jul 6, 2017
Publication date	Feb 14, 2023
Grant date	Feb 14, 2023

Abstract

A method for dynamically quantizing feature maps of a received image. The method includes convolving an image based on a predicted maximum value, a predicted minimum value, trained kernel weights and the image data. The input data is quantized based on the predicted minimum value and predicted maximum value. The output of the convolution is computed into an accumulator and re-quantized. The re-quantized value is output to an external memory. The predicted min value and the predicted max value are computed based on the previous max values and min values with a weighted average or a pre-determined formula. Initial min value and max value are computed based on known quantization methods and utilized for initializing the predicted min value and predicted max value in the quantization process.

First claim

We claim:

1. A method comprising:
receiving a set of input values within a first range having a first bit depth;
performing a convolution operation on the set of input values to produce a set of intermediate values having a second bit depth that is different from the first bit depth;
re-quantizing the set of intermediate values by performing a division operation on the set of intermediate values to produce a set of output values within a second range having the first bit depth;
determining a predicted maximum for the second range based on a maximum of the set of input values; and
determining a predicted minimum for the second range based on a minimum of the set of input values.

2. The method of claim 1 , wherein:
the set of input values is a first set of input values;
the receiving includes receiving a plurality of sets of input values that includes the first set of input values;
the determining of the predicted maximum for the second range includes:
determining a respective maximum of each set of the plurality of sets of input values; and
averaging the respective maximums; and

the determining of the predicted minimum for the second range includes:
determining a respective minimum of each set of the plurality of sets of input values; and
averaging the respective minimums.

3. The method of claim 2 , wherein the plurality of sets of input values are associated with a plurality of images.

4. The method of claim 1 , wherein:
the set of input values is a first set of input values;
the receiving includes receiving a plurality of sets of input values that includes the first set of input values;
the performing of the convolution operation performs the convolution operation on each set of the plurality of sets of input values to produce a respective set of intermediate values;
the determining of the predicted maximum for the second range includes:
determining a respective maximum of each of the respective sets of intermediate values; and
determining an initial maximum based on the respective maximums; and

the determining of the predicted minimum for the second range includes:
determining a respective minimum of each of the respective sets of intermediate values; and
determining an initial minimum based on the respective minimums.

5. The method of claim 4 , wherein each set of the plurality of sets of input values is associated with a respective layer of a feature map.

6. The method of claim 4 , wherein the determining of the initial maximum and the initial minimum are performed based on the first set of input values being associated with a first image in a sequence.

7. The method of claim 1 , wherein the convolution operation includes:
receiving a set of weightings;
applying the set of weightings to the set of input values to produce a weighted set of input values; and
applying a finite impulse response filter operation to the weighted set of input values.

8. The method of claim 1 , wherein the first bit depth is 8 bits and the second bit depth is 32 bits.

9. An integrated circuit comprising:
an input configured to receive a set of input values within a first range and having a first bit depth;
a convolution circuit coupled to the input and configured to:
perform a convolution operation on the set of input values to produce a set of intermediate values having a second bit depth that is different from the first bit depth; and
determine a second range by:
determining a predicted maximum for the second range based on a maximum of the set of input values; and
determining a predicted minimum for the second range based on a minimum of the set of input values; and

a re-quantization circuit coupled to the convolution circuit and configured to re-quantize the set of intermediate values by performing a division operation on the set of intermediate values to produce a set of output values within the second range having the first bit depth.

10. The integrated circuit of claim 9 , wherein:
the set of input values is a first set of input values; and
the convolution circuit is configured to:
determine the predicted maximum by:
determining a respective maximum of each set of a plurality of sets of input values that includes the first set of input values; and
averaging the respective maximums; and

determine the predicted minimum by:
determining a respective minimum of each set of the plurality of sets of input values; and
averaging the respective minimums.

11. The integrated circuit of claim 10 , wherein the plurality of sets of input values are associated with a plurality of images.

12. The integrated circuit of claim 9 , wherein:
the set of input values is a first set of input values; and
the convolution circuit is configured to:
perform the convolution operation on each set of a plurality of sets of input values that includes the first set of input values to produce a respective set of intermediate values;
determine the predicted maximum by:

determining a respective maximum of each of the respective sets of intermediate values; and
determining an initial maximum based on the respective maximums; and
determine the predicted minimum by:
determining a respective minimum of each of the respective sets of intermediate values; and
determining an initial minimum based on the respective minimums.

13. The integrated circuit of claim 12 , wherein each set of the plurality of sets of input values is associated with a respective layer of a feature map.

14. The integrated circuit of claim 12 , wherein the convolution circuit is configured to perform the determination of the initial maximum and the initial minimum based on the first set of input values being associated with a first image in a sequence.

15. The integrated circuit of claim 9 , wherein the convolution circuit is configured to perform the convolution operation by:
applying a set of weightings to the set of input values to produce a weighted set of input values; and
applying a finite impulse response filter operation to the weighted set of input values.

16. The integrated circuit of claim 9 , wherein the first bit depth is 8 bits and the second bit depth is 32 bits.

17. An integrated circuit comprising:
an input configured to receive a set of input values within a first range and having a first bit depth;
a convolution circuit coupled to the input and configured to:
perform a convolution operation on the set of input values to produce a set of intermediate values having a second bit depth that is different from the first bit depth; and
determine a second range by:
determining a predicted maximum for the second range based on a maximum of the set of input values; and
determining a predicted minimum for the second range based on a minimum of the set of input values; and

a re-quantization circuit coupled to the convolution circuit and configured to re-quantize the set of intermediate values by performing a shift on the set of intermediate values to produce a set of output values within the second range having the first bit depth.

18. The integrated circuit of claim 17 , wherein the set of input values is associated with a feature map.

19. The integrated circuit of claim 17 , wherein the convolution circuit is configured to perform the convolution operation by:
applying a set of weightings to the set of input values to produce a weighted set of input values; and
applying a finite impulse response filter operation to the weighted set of input values.

20. The integrated circuit of claim

Assignees

• Texas Instruments Inc

Inventors

• Desappan Kumar
• Mathew Manu
• Swami Pramod Kumar
• Eppa Praveen

Classifications

• G06N3/0464

  Convolutional networks [CNN, ConvNet] · CPC title

• G06N3/08

  Learning methods · CPC title

• G06N3/0495

  Quantised networks; Sparse networks; Compressed networks · CPC title

• G06N3/09

  Supervised learning · CPC title

• G06V10/454

  Integrating the filters into a hierarchical structure, e.g. convolutional neural networks [CNN] · CPC title