WEIGHT-SHIFTING MECHANISM FOR CONVOLUTIONAL NEURAL NETWORKS

• US 20160026912A1
• Filed: 07/22/2014
• Published: 01/28/2016
• Est. Priority Date: 07/22/2014
• Status: Abandoned Application

• Current Assignee: Intel Corporation
• Original Assignee: Intel Corporation
• Inventors: Falcon, Ayose J., Lupon, Marc, Herrero Abellanas, Enric, Lopez, Pedro, Latorre, Fernando, Pratas, Frederico C., Tournavitis, Georgios
• Application Number:
  US14/337,979
• Publication Number:
  US 20160026912A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06N 3/045 Combinations of networks
• CPC Class Codes: G06N 3/06 Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons
• CPC Class Codes: G06N 3/063 using electronic means
• CPC Class Codes: G06N 3/08 Learning methods

Abstract

A processor includes a processor core and a calculation circuit. The processor core includes logic determine a set of weights for use in a convolutional neural network (CNN) calculation and scale up the weights using a scale value. The calculation circuit includes logic to receive the scale value, the set of weights, and a set of input values, wherein each input value and associated weight of a same fixed size. The calculation circuit also includes logic to determine results from convolutional neural network (CNN) calculations based upon the set of weights applied to the set of input values, scale down the results using the scale value, truncate the scaled down results to the fixed size, and communicatively couple the truncated results to an output for a layer of the CNN.

Claims

1. A processor, comprising:

a processor core including;

a first logic to determine a set of weights for use in a convolutional neural network (CNN) calculation;

a second logic to scale up the weights using a scale value; and

a calculation circuit including;

a third logic to receive the scale value, the set of weights, and a set of input values, each input value and associated weight of a same fixed size;

a fourth logic to determine results from CNN calculations based upon the set of weights applied to the set of input values;

a fifth logic to scale down the results using the scale value;

a sixth logic to truncate the scaled down results to the fixed size; and

a seventh logic to communicatively couple the truncated results to an output for a layer of the CNN.

2. The processor of claim 1, wherein the processor core further includes an eighth logic to truncate the scaled up weights to the fixed size.

3. The processor of claim 1, wherein the processor core further includes an eighth logic to scale up all weights with the same scale value for a given layer of the CNN.

4. The processor of claim 1, wherein the processor core further includes an eighth logic to scale up the weights to a fixed interval of values.

5. The processor of claim 1, wherein the calculation unit further includes an eighth logic to shift bits of the results to the right in order to scale down the results, the scale value indicating the number of bits to be shifted.

6. The processor of claim 1, wherein the calculation unit further includes an eighth logic to store the scaled down results as partial results for future calculations.

7. The processor of claim 1, wherein the calculation unit further includes:

an eighth logic to receive partial results from a previous calculation;

a ninth logic to scale up the partial results using the scale factor;

a tenth logic to determine the results from CNN calculations further based upon the partial results.

8. A system, comprising:

a processor core including;

a first logic to determine a set of weights for use in a convolutional neural network (CNN) calculation;

a second logic to scale up the weights using a scale value; and

a calculation circuit including;

a third logic to receive the scale value, the set of weights, and a set of input values, each input value and associated weight of a same fixed size;

a fourth logic to determine results from CNN calculations based upon the set of weights applied to the set of input values;

a fifth logic to scale down the results using the scale value;

a sixth logic to truncate the scaled down results to the fixed size; and

a seventh logic to communicatively couple the truncated results to an output for a layer of the CNN.

9. The system of claim 8, wherein the processor core further includes an eighth logic to truncate the scaled up weights to the fixed size.

10. The system of claim 8, wherein the processor core further includes an eighth logic to scale up all weights with the same scale value for a given layer of the CNN.

11. The system of claim 8, wherein the processor core further includes an eighth logic to scale up the weights to a fixed interval of values.

12. The system of claim 8, wherein the calculation unit further includes an eighth logic to shift bits of the results to the right in order to scale down the results, the scale value indicating the number of bits to be shifted.

13. The system of claim 8, wherein the calculation unit further includes an eighth logic to store the scaled down results as partial results for future calculations.

14. The system of claim 8, wherein the calculation unit further includes:

an eighth logic to receive partial results from a previous calculation;

a ninth logic to scale up the partial results using the scale factor;

a tenth logic to determine the results from CNN calculations further based upon the partial results.

15. A method for security, comprising:

determining a set of weights for use in a convolutional neural network (CNN) calculation;

scaling up the weights using a scale value and routing the weights to a calculation circuit;

receiving the scale value, the set of weights, and a set of input values at the calculation circuit, each input value and associated weight of a same fixed size;

determining results from CNN calculations based upon the set of weights applied to the set of input values;

scaling down the results using the scale value;

truncating the scaled down results to the fixed size; and

communicatively coupling the truncated results to an output for a layer of the CNN.

16. The method of claim 15, further comprising truncating the scaled up weights to the fixed size.

17. The method of claim 15, further comprising scaling up all weights with the same scale value for a given layer of the CNN.

18. The method of claim 15, further comprising scaling up the weights to a fixed interval of values.

19. The method of claim 15, further comprising shifting bits of the results to the right in order to scale down the results, the scale value indicating the number of bits to be shifted.

20. The method of claim 15, further comprising:

receiving partial results from a previous calculation;

scaling up the partial results using the scale factor;

determining the results from CNN calculations further based upon the partial results.