公开(公告)号：US20250036975A1

公开(公告)日：2025-01-30

申请号：US18414057

申请日：2024-01-16

Applicant: NVIDIA Corporation

Inventor： Xiaowei Ren ,  Nitin Nitin ,  Michael Andersch

IPC: G06N5/04

Abstract: Apparatuses, systems, and techniques to perform neural networks. In at least one embodiment, a processor is to cause information to be distributed to processing cores. In at least one embodiment, a processor is to cause inferencing of two or more contiguous portions of information to be distributed between two or more respective processing cores based, at least in part, on locations of the two or more contiguous portions within the information relative to one or more terminating portions of the information.

公开(公告)号：US20240152725A1

公开(公告)日：2024-05-09

申请号：US17982386

申请日：2022-11-07

Applicant: NVIDIA Corporation

Inventor： Gil Shomron ,  Rachit Garg ,  Sukru Burc Eryilmaz ,  Michael Andersch

IPC: G06N3/04

CPC classification number: G06N3/04

Abstract: Apparatuses, systems, and techniques to perform matrix computations associated with computing output of a neural network. In at least one embodiment, one or more circuits cause one or more feature maps of one or more neural networks to be spatially concatenated.

公开(公告)号：US20250036954A1

公开(公告)日：2025-01-30

申请号：US18226143

申请日：2023-07-25

Applicant: NVIDIA Corporation

Inventor： Xiaowei Ren ,  Kevin Chong Man Siu ,  Nitin Nitin ,  Michael Andersch

IPC: G06N3/084 ,  G06N3/04

Abstract: Apparatuses, systems, and techniques to perform neural networks. In at least one embodiment, a processor is to cause information to be distributed to processing cores. In at least one embodiment, a processor is to cause inferencing of two or more contiguous portions of information to be distributed only between two or more respective processing cores.

公开(公告)号：US09684581B2

公开(公告)日：2017-06-20

申请号：US14283700

申请日：2014-05-21

Applicant: NVIDIA CORPORATION

Inventor： Andrew Robert Kerr ,  Matthew Grant Bolitho ,  Igor Sevastiyanov ,  Scott Ricketts ,  Michael Andersch

IPC: G06F9/46 ,  G06F11/34 ,  G06F9/455 ,  G06F11/36 ,  G06F9/45

CPC classification number: G06F11/3428 ,  G06F8/433 ,  G06F9/455 ,  G06F11/34 ,  G06F11/3452 ,  G06F11/3466 ,  G06F11/3612

Abstract: One embodiment of the present invention includes a dependency extractor and a dependency investigator that, together, facilitate performance analysis of computer systems. In operation, the dependency extractor instruments a software application to generate run-time execution data for each work task. This execution data includes per-task performance data and dependency data reflecting linkages between tasks. After the instrumented software application finishes executing, the dependency investigator evaluates the captured execution data and identifies the critical path of tasks that establishes the overall run-time of the software application. Advantageously, since the execution data includes both task-level performance data and dependencies between tasks, the dependency investigator enables the developer to effectively optimize software and hardware in computer systems that are capable of concurrently executing tasks. By contrast, conventional performance analysis may not correctly identify critical paths in software applications that execute tasks in parallel across multiple processing units and, consequently, may misdirect optimization efforts.