Granted Patent
B2
US 12,271,339 · App. 18/483,026 · Granted Apr 8, 2025
Instruction format and instruction set architecture for tensor streaming processor
Inventors:
Dennis Charles Abts (Eau Claire, WI); Jonathan Alexander Ross (Palo Alto, CA); John Thompson (Minneapolis, MN); Gregory Michael Thorson (Palo Alto, CA)
Assignee:
Groq, Inc.
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Abstract
Embodiments are directed to a processor having a functional slice architecture. The processor is divided into tiles (or functional units) organized into a plurality of functional slices. The functional slices are configured to perform specific operations within the processor, which includes memory slices for storing operand data and arithmetic logic slices for performing operations on received operand data (e.g., vector processing, matrix manipulation). The processor includes a plurality of functional slices of a module type, each functional slice having a plurality of tiles. The processor further includes a plurality of data transport lanes for transporting data in a direction indicated in a corresponding instruction. The processor also includes a plurality of instruction queues, each instruction queue associated with a corresponding functional slice of the plurality of functional slices, wherein the instructions in the instruction queues comprise a functional slice specific operation code.
Claims (23)
1. A compiler for operating a tensor streaming processor comprising: compiling a plurality of instructions for operation of the tensor streaming processor by the compiler operating on at least one computer processor and storing the plurality of instructions in a tangible storage medium, wherein the compiler:
allocates functional resources and data transport lanes of the tensor streaming processor for performing a selected operation as determined by each of the plurality of instructions,
schedules processing of one or more data flows through specific functional resources of the tensor streaming processor, and
schedules instruction flow for a plurality of instruction flows, each instruction flow causing a selected one or more functional resources to execute a specific instruction using specific data at a defined time instant.
2. The compiler of claim 1 , wherein the compiler is hardware aware such that corresponding data and instructions intersect at the selected one or more functional resources at the defined time instant when the instructions are executed by the one or more functional resources of the tensor streaming processor.
3. The compiler of claim 2 , wherein the compiler schedules instruction flows that comprise intermittent delivery of at least one functional slice specific opcode to a first tile and to at least a second tile.
4. The compiler of claim 1 , wherein the functional resources comprise a first tile and at least a second tile having a predetermined temporal spatial relationship, and wherein the compiler synchronizes a timing of data and instruction flows such that corresponding data and instructions are received at the first tile and at least the second tile based on the predetermined temporal spatial relationship.
5. The compiler of claim 4 , wherein the first tile and at least the second tile operate without having to receive functional slice explicit opcodes from the instruction flow.
6. The compiler of claim 5 , wherein the compiler schedules one or more data flows.
7. The compiler of claim 6 , wherein the compiler schedules one or more processed data flows.
8. The compiler of claim 1 , wherein the compiler schedules one or more data flows and processed data flows along one or more data transport lanes.
9. A method for operating a tensor streaming processor comprising:
compiling a plurality of instructions for operation of the tensor streaming processor by a compiler operating on at least one computer processor and storing the plurality of instructions in a tangible storage medium, wherein the compiler:
allocates functional resources and data transport lanes of the tensor streaming processor for performing a selected operation as determined by each of the plurality of instructions,
schedules processing of one or more data flows through specific functional resources of the tensor streaming processor, and
schedules instruction flow for a plurality of instruction flows, each instruction flow causing a selected one or more functional resources to execute a specific instruction using specific data at a defined time instant.
10. The method of claim 9 , wherein the compiler is hardware aware such that corresponding data and instructions intersect at the selected one or more functional resources at the defined time instant when the instructions are executed by the one or more functional resources of the tensor streaming processor.
11. The method of claim 10 , wherein the compiler schedules instruction flows that comprise intermittent delivery of at least one functional slice specific opcode to a first tile and to at least one second tile.
12. The method of claim 10 , wherein the functional resources comprise a first tile and at least a second tile having a predetermined temporal spatial relationship, and wherein the compiler synchronizes a timing of data and instruction flows such that corresponding data and instructions are received at the first tile and at least the second tile based on the predetermined temporal spatial relationship.
13. The method of claim 12 , wherein the first tile and at least the second tile operate without having to receive functional slice explicit opcodes from the instruction flow.
14. The method of claim 13 , wherein the compiler schedules one or more data flows.
15. The method of claim 14 , wherein the compiler schedules one or more processed data flows.
16. The method of claim 9 , wherein the compiler schedules one or more data flows and processed data flows along one or more data transport lanes.
Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST
Recorded Oct 9, 2023
From: ABTS, DENNIS CHARLES; ROSS, JONATHAN ALEXANDER; THOMPSON, JOHN; THORSON, GREGORY MICHAEL
To: GROQ, INC
Reel/Frame 065158/0471 →
Continuity (11)
Continuation
17684337
· Mar 1, 2022
Continuation
17105976
· Nov 27, 2020
Continuation In Part
17103910
· Nov 24, 2020
Continuation In Part
16951938
· Nov 18, 2020
Continuation In Part
16526966
· Jul 30, 2019
Continuation In Part
16132243
· Sep 14, 2018
Provisional Application
63114500
· Nov 16, 2020
Provisional Application
62940815
· Nov 26, 2019
Provisional Application
62937123
· Nov 18, 2019
Provisional Application
62559333
· Sep 15, 2017
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