Granted Patent B2

US 12,277,499 · App. 18/636,640 · Granted Apr 15, 2025

Inventors: Gregory Michael Thorson (Waunakee, WI); Christopher Aaron Clark (Madison, WI); Dan Luu (Madison, WI)

Assignee: Google LLC

G06N3/08G06F5/08G06F7/544G06N3/063G06N5/04

Abstract

Claims (54)

1. A vector computation unit for performing neural network computations comprising:

normalization circuitry configured to:

receive a vector of activation values;

receive one or more control signals specifying a normalization function; and

apply the normalization function to the activation values to generate respective normalized values for each activation value.

2. The vector computation unit of claim 1 , wherein the one or more control signals are provided by a sequencer.

3. The vector computation unit of claim 1 , further comprising activation circuitry configured to:

receive a vector of accumulated values;

receive one or more control signal specifying an activation function; and

apply the activation function to the accumulated values to generate the vector of activation values.

4. The vector computation unit of claim 3 , wherein the accumulated values correspond to products of a matrix multiplication between a layer of the neural network and a parameter matrix for the layer.

5. The vector computation unit of claim 1 , further comprising pooling circuitry configured to:

receive the normalized values;

receive one or more control signals specifying a pooling function; and

apply the pooling function to the normalized values to generate a pooled value.

6. The vector computation unit of claim 5 , wherein the pooled value comprises at least one of a maximum, a minimum, or an average of the activation values, or a maximum, a minimum, or an average of a subset of the activate values.

7. The vector computation unit of claim 5 , wherein the pooling circuitry comprises multiple parallel pooling circuitries, each pool circuitry configured to receive a subset of the activation values to generate a respective pooled value.

8. The vector computation unit of claim 1 , wherein applying the normalization function to the activation values comprises:

determining a sum of the activation values;

determining a multiplication factor based on the sum of the activation values; and

multiplying the activation values by the multiplication factor to generate the normalized values.

9. The vector computation unit of claim 1 , further comprising a plurality of registers and a plurality of memory units configured to store the activation values.

10. A method for performing neural network computations comprising:

receiving, by normalization circuitry of a vector computation unit, a vector of activation values;

receiving, by the normalization circuitry, one or more control signals specifying a normalization function; and

applying, by the normalization circuitry, the normalization function to the activation values to generate respective normalized values for each activation value.

11. The method of claim 10 , wherein the one or more control signals are provided by a sequencer.

12. The method of claim 10 , further comprising:

receiving, by activation circuitry of the vector computation unit, a vector of accumulated values;

receiving, by the activation circuitry, one or more control signal specifying an activation function; and

applying, by the activation circuitry, the activation function to the accumulated values to generate the vector of activation values.

13. The method of claim 12 , wherein the accumulated values correspond to products of a matrix multiplication between a layer of the neural network and a parameter matrix for the layer.

14. The method of claim 10 , further comprising:

receiving, by pooling circuitry of the vector computation unit, the normalized values;

receiving, by the pooling circuitry, one or more control signals specifying a pooling function; and

applying, by the pooling circuitry, the pooling function to the normalized values to generate a pooled value.

15. The method of claim 14 , wherein the pooled value comprises at least one of a maximum, a minimum, or an average of the activation values, or a maximum, a minimum, or an average of a subset of the activate values.

16. The method of claim 14 , wherein the pooling circuitry comprises multiple parallel pooling circuitries, each pool circuitry configured to receive a subset of the activation values to generate a respective pooled value.

17. The method of claim 10 , wherein applying the normalization function to the activation values comprises:

determining a sum of the activation values;

determining a multiplication factor based on the sum of the activation values; and

multiplying the activation values by the multiplication factor to generate the normalized values.

18. A non-transitory computer readable medium for storing instructions executable by a processor to perform neural network computations, the instructions comprising:

receiving a vector of activation values;

receiving one or more control signals specifying a normalization function; and

applying the normalization function to the activation values to generate respective normalized values for each activation value.

19. The non-transitory computer readable medium of claim 18 , wherein the instructions further comprise:

receiving a vector of accumulated values;

receiving one or more control signal specifying an activation function; and

applying the activation function to the accumulated values to generate the vector of activation values.

20. The non-transitory computer readable medium of claim 18 , wherein the instructions further comprise:

receiving the normalized values;

receiving one or more control signals specifying a pooling function; and

applying the pooling function to the normalized values to generate a pooled value.

Assignments (2)

Continuity (5)

References Cited (152)