Predictive control using one or more neural networks

Patent metadata

Field	Value
Publication number	US-2022087075-A1
Application number	US-202017023695-A
Country	US
Kind code	A1
Filing date	Sep 17, 2020
Priority date	Sep 17, 2020
Publication date	Mar 17, 2022
Grant date	—

Abstract

Systems and methods for cooling a computer environment are disclosed. In at least one embodiment, one or more neural networks can be used to determine one or more temperature control settings associated with one or more servers.

First claim

What is claimed is:

1 . A processor, comprising:
one or more circuits to use one or more neural networks to determine one or more temperature control settings associated with one or more servers.

2 . The processor of claim 1 , wherein the one or more neural networks are further to predict one or more future temperature values at one or more locations at one or more future points in time, and wherein the one or more temperature control settings are determined based at least in part upon the one or more future temperature values.

3 . The processor of claim 1 , wherein the one or more neural networks are stored on one or more computing boards in one or more temperature control devices associated with the one or more temperature control settings.

4 . The processor of claim 1 , wherein the one or more temperature control settings are determined for at least one of: individual servers of the one or more servers, individual racks including the one or more servers, or a data center including the one or more servers, and wherein the one or more neural networks can further be used to determine one or more environmental control settings relating to at least one of power, humidity, fluid flow, or power.

5 . The processor of claim 1 , wherein the one or more circuits are further to utilize the one or more neural networks over time to determine whether to update the one or more temperature control settings based upon updated predictions generated by the one or more neural networks.

6 . The processor of claim 1 , wherein the one or more neural networks accept environmental data from at least one of a temperature sensor, a pressure sensor, a flow sensor, a power sensor, a humidity sensor, or a load determination component associated with the one or more servers.

7 . A system comprising:
one or more processors to use one or more neural networks to determine one or more temperature control settings associated with one or more servers.

8 . The system of claim 7 , wherein the one or more neural networks are further to predict one or more future temperature values at one or more locations at one or more future points in time, and wherein the one or more temperature control settings are determined based at least in part upon the one or more future temperature values.

9 . The system of claim 7 , wherein the one or more neural networks are stored on one or more computing boards in one or more temperature control devices associated with the one or more temperature control settings.

10 . The system of claim 7 , wherein the one or more temperature control settings are determined for at least one of: individual servers of the one or more servers, individual racks including the one or more servers, or a data center including the one or more servers, and wherein the one or more neural networks can further be used to determine one or more environmental control settings relating to at least one of power, humidity, fluid flow, or power.

11 . The system of claim 7 , wherein the one or more processors are further to utilize the one or more neural networks over time to determine whether to update the one or more temperature control settings based upon updated predictions generated by the one or more neural networks.

12 . The system of claim 7 , wherein the one or more neural networks accept environmental data from at least one of a temperature sensor, a pressure sensor, a flow sensor, a power sensor, a humidity sensor, or a load determination component associated with the one or more servers.

13 . A method comprising:
using one or more neural networks to determine one or more temperature control settings associated with one or more servers.

14 . The method of claim 13 , wherein the one or more neural networks are further to predict one or more future temperature values at one or more locations at one or more future points in time, and wherein the one or more temperature control settings are determined based at least in part upon the one or more future temperature values.

15 . The method of claim 13 , wherein the one or more neural networks are stored on one or more computing boards in one or more temperature control devices associated with the one or more temperature control settings.

16 . The method of claim 13 , wherein the one or more temperature control settings are determined for at least one of: individual servers of the one or more servers, individual racks including the one or more servers, or a data center including the one or more servers, and wherein the one or more neural networks can further be used to determine one or more environmental control settings relating to at least one of power, humidity, fluid flow, or power.

17 . The method of claim 13 , further comprising:
utilizing the one or more neural networks over time to determine whether to update the one or more temperature control settings based upon updated predictions generated by the one or more neural networks.

18 . The method of claim 13 , wherein the one or more neural networks accept environmental data from at least one of a temperature sensor, a pressure sensor, a flow sensor, a power sensor, a humidity sensor, or a load determination component associated with the one or more servers.

19 . A machine-readable medium having stored thereon a set of instructions, which if performed by one or more processors, cause the one or more processors to at least:
use one or more neural networks to determine one or more temperature control settings associated with one or more servers.

20 . The machine-readable medium of claim 19 , wherein the one or more neural networks are further to predict one or more future temperature values at one or more locations at one or more future points in time, and wherein the one or more temperature control settings are determined based at least in part upon the one or more future temperature values.

21 . The machine-readable medium of claim 19 , wherein the one or more neural networks are stored on one or more computing boards in one or more temperature control devices associated with the one or more temperature control settings.

22 . The machine-readable medium of claim 19 , wherein the one or more temperature control settings are determined for at least one of: individual servers of the one or more servers, individual racks including the one or more servers, or a data center including the one or more servers, and wherein the one or more neural networks can further be used to determine one or more environmental control settings relating to at least one of power, humidity, fluid flow, or power.

23 . The machine-readable medium of claim 19 , wherein the instructions if performed further cause the one or more processors to:
utilize the one or more neural networks over time to determine whether to update the one or more temperature control settings based upon updated predictions generated by the one or more neural networks.

24 . The machine-readable medium of claim 19 , wherein the one or more neural networks accept environmental data from at least one of a temperature sensor, a pressure sensor, a flow sensor, a power sensor, a humidity sensor, or a load determination component associated with the one or more servers.

25 . A data center cooling system, comprising:
one or more cooling systems associated with one or more servers; one or more processors to use one or more neural networks to determine one or more temperatur

Assignees

• Nvidia Corp

Inventors

• Heydari Ali
• Troy Vladimir

Classifications

• G06N3/045 Primary

  Combinations of networks · CPC title

• G06N3/09

  Supervised learning · CPC title

• H05K7/20836 Primary

  Thermal management, e.g. server temperature control · CPC title

• H05K7/20772

  within server blades for removing heat from heat source · CPC title

• H05K7/20727

  within server blades for removing heat from heat source · CPC title