Granted Patent B2

US 12,217,056 · App. 18/423,210 · Granted Feb 4, 2025

Inventor: Philip Winterbottom (San Jose, CA)

Assignee: Celestial AI Inc.

G06F9/30036G06F9/30065G06F9/3013

Abstract

Claims (36)

1. A method for processing a tensor, comprising:

obtaining a first register for a number of items in the tensor;

obtaining one or more second registers for a number of items in a first and a second axis of the tensor;

obtaining a stride in the first and the second axis;

obtaining a next item in the tensor using the stride in the first axis and a first offset register, when the first register indicates the tensor has additional items to process and the second registers indicate the next item resides in the first axis;

obtaining a next item in the tensor using the stride in the first axis and the second axis, the first offset register, and a second offset register, when the first register indicates the tensor has additional items to process, and the second registers indicate the next item resides in the second axis of the tensor;

modifying the first register and one or more of the second registers; and

modifying at least one of the first and the second offset registers.

2. The method of claim 1 , further comprising determining an effective address of the next item using a base address for the tensor in a memory region and one or more values stored in the first and the second offset registers.

3. The method of claim 2 , further comprising reading the next item at the effective address.

4. The method of claim 2 , further comprising writing the next item to the effective address.

5. The method of claim 1 , wherein obtaining a next item in the tensor in a first axis further comprises determining an order for traversing the tensor.

6. The method of claim 2 , wherein determining an effective address of the next item using a base address for the tensor in a memory region and one or more values stored in the first and the second offset registers further comprises:

setting a channel, a height, or a width as the first axis.

7. The method of claim 1 , wherein obtaining a stride uses a primitive data type associated with the tensor.

8. The method of claim 7 , wherein the primitive data type is selected from the group consisting of bits, bytes, integers, words, boolean values, (brain floating-point) BF-16, or FP-32.

9. The method of claim 1 , wherein modifying the first register and one or more of the second registers includes incrementing or decrementing the first register and one or more of the second registers.

10. The method of claim 1 , wherein modifying at least one of the first and the second offset registers includes one or more of adding a first value in the first offset register to the stride in the first axis and adding a second value in the second offset register to the stride in the second axis.

11. A load/store unit (LDSU) for a tensor engine comprising:

a first register for storing values associated with a number of items in a tensor;

a plurality of second registers for storing a number of items in a first and a second axis of the tensor;

a plurality of offset registers associated with the first and the second axis;

a first and a second stride register associated with the first and the second axis;

a tensor walking module configured to obtain a next item in the tensor using a first stride register and a first offset register, when the first register indicates the tensor has additional items to process and the second registers indicate the next item resides in the first axis of the tensor,

the tensor walking module further configured to obtain a next item in the tensor using the first and the second stride registers, the first offset register, and a second offset register, when the first register indicates the tensor has additional items to process, and the second registers indicate the next item resides in the second axis of the tensor;

an iteration tracking module configured to modify the first and the second registers; and

a striding module configured to modify at least one of the first offset register or the second offset register.

12. The LDSU of claim 11 , comprising an address module configured to determine an effective address of the next item using a base address for the tensor in a memory region and one or more values stored in the first and the second offset registers.

13. The LDSU of claim 12 , further comprising an access module is configured to read the next item at the effective address.

14. The LDSU of claim 13 , wherein the access module is further configured to write the next item to the effective address.

15. The LDSU of claim 11 , further comprising a layout module is used to determine an order for traversing a plurality of dimensions of the tensor.

16. The LDSU of claim 15 , wherein a first dimension of the plurality of dimensions is selected from the group consisting of a channel, a height, and a width.

17. The LDSU of claim 11 , wherein the striding module uses an item type associated with the tensor to modify the first offset register or the second offset register.

18. The LDSU of claim 17 , wherein the item type is selected from the group consisting of bits, bytes, integers, words, boolean values, (brain floating-point) BF-16, or FP-32.

19. The LDSU of claim 11 , wherein the iteration tracking module modifies the first and second registers by incrementing or decrementing values in the first and the second registers.

20. The LDSU of claim 11 , wherein the striding module modifies at least one of the first offset register or the second offset register by adding a first value in the first offset register to a second value in the first stride register and adding a third value in the second offset register to a fourth value in the second stride register.

Assignments (2)

Continuity (2)

References Cited (308)

Cited By (1)

US 12,663,580