公开(公告)号：US20230079978A1

公开(公告)日：2023-03-16

申请号：US17709720

申请日：2022-03-31

Applicant: Nvidia Corporation

Inventor： Evgeny Bolotin ,  Yaosheng Fu ,  Zi Yan ,  Gal Dalal ,  Shie Mannor ,  David Nellans

IPC: G06F1/324 ,  G06F1/20 ,  G06F11/34

Abstract: A system, method, and apparatus of power management for computing systems are included herein that optimize individual frequencies of components of the computing systems using machine learning. The computing systems can be tightly integrated systems that consider an overall operating budget that is shared between the components of the computing system while adjusting the frequencies of the individual components. An example of an automated method of power management includes: (1) learning, using a power management (PM) agent, frequency settings for different components of a computing system during execution of a repetitive application, and (2) adjusting the frequency settings of the different components using the PM agent, wherein the adjusting is based on the repetitive application and one or more limitations corresponding to a shared operating budget for the computing system.

公开(公告)号：US11609879B2

公开(公告)日：2023-03-21

申请号：US17365315

申请日：2021-07-01

Applicant: NVIDIA CORPORATION

Inventor： Yaosheng Fu ,  Evgeny Bolotin ,  Niladrish Chatterjee ,  Stephen William Keckler ,  David Nellans

IPC: G06F15/78 ,  G06F12/0811 ,  G06F12/12 ,  G06F13/40

Abstract: In various embodiments, a parallel processor includes a parallel processor module implemented within a first die and a memory system module implemented within a second die. The memory system module is coupled to the parallel processor module via an on-package link. The parallel processor module includes multiple processor cores and multiple cache memories. The memory system module includes a memory controller for accessing a DRAM. Advantageously, the performance of the parallel processor module can be effectively tailored for memory bandwidth demands that typify one or more application domains via the memory system module.

公开(公告)号：US11880261B2

公开(公告)日：2024-01-23

申请号：US17709720

申请日：2022-03-31

Applicant: Nvidia Corporation

Inventor： Evgeny Bolotin ,  Yaosheng Fu ,  Zi Yan ,  Gal Dalal ,  Shie Mannor ,  David Nellans

IPC: G06F1/324 ,  G06F11/34 ,  G06F1/20

CPC classification number: G06F1/324 ,  G06F1/206 ,  G06F11/3495

Abstract: A system, method, and apparatus of power management for computing systems are included herein that optimize individual frequencies of components of the computing systems using machine learning. The computing systems can be tightly integrated systems that consider an overall operating budget that is shared between the components of the computing system while adjusting the frequencies of the individual components. An example of an automated method of power management includes: (1) learning, using a power management (PM) agent, frequency settings for different components of a computing system during execution of a repetitive application, and (2) adjusting the frequency settings of the different components using the PM agent, wherein the adjusting is based on the repetitive application and one or more limitations corresponding to a shared operating budget for the computing system.

公开(公告)号：US20230137205A1

公开(公告)日：2023-05-04

申请号：US17514735

申请日：2021-10-29

Applicant: Nvidia Corporation

Inventor： Yaosheng Fu ,  Shie Mannor ,  Evgeny Bolotin ,  David Nellans ,  Gal Dalal

IPC: G06F12/123 ,  G06N20/00 ,  G06T1/60

Abstract: Introduced herein is a technique that uses ML to autonomously find a cache management policy that achieves an optimal execution of a given workload of an application. Leveraging ML such as reinforcement learning, the technique trains an agent in an ML environment over multiple episodes of a stabilization process. For each time step in these training episodes, the agent executes the application while making an incremental change to the current policy, i.e., cache-residency statuses of memory address space associated with the workload, until the application can be executed at a stable level. The stable level of execution, for example, can be indicated by performance variations, such as standard deviations, between a certain number of neighboring measurement periods remaining within a certain threshold. The agent, who has been trained in the training episodes, infers the final cache management policy during the final, inferring episode.