# US 2019/0318264 A1 · Automated thermal policy tuning

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- Number: US 2019/0318264 A1
- Document ID: US20190318264A1
- Status: Published application
- Kind: A1
- Application number: 16/455,407
- Filed: 2019-06-27
- Published: 2019-10-17
- Earliest claimed: 2019-06-27
- Inventors: Qiyong Brian Bian; James Hermerding; Zhongsheng Wang; Helin Cao
- Web page: https://patentgrep.com/patent/US20190318264A1

## CPC

- G06N 20/00: Machine learning
- G06F 1/206: {comprising thermal management}
- G06N 3/006: based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N)

## Abstract

Various systems and methods for implementing automatic thermal policy tuning are described herein. A system for thermal policy tuning on an electronic device, comprising: a memory device configured to store instructions; and a processor subsystem, which when configured by the instructions, is operable to perform the operations comprising: accessing a thermal policy configuration comprising a plurality of parameters to control a thermal policy of the electronic device; using the thermal policy configuration as input to a machine-learning algorithm, the machine-learning algorithm using an objective function to determine a revised thermal policy configuration; and implementing the revised thermal policy configuration on the electronic device.

## Claims

### Claim 1

A system for thermal policy tuning on an electronic device, comprising:
a memory device configured to store instructions; and
a processor subsystem, which when configured by the instructions, is operable to perform the operations comprising:
accessing a thermal policy configuration comprising a plurality of parameters to control a thermal policy of the electronic device;
using the thermal policy configuration as input to a machine-learning algorithm, the machine-learning algorithm using an objective function to determine a revised thermal policy configuration; and
implementing the revised thermal policy configuration on the electronic device.

### Claim 2 (depends on 1)

The system of claim 1, wherein the electronic device comprises a processor.

### Claim 3 (depends on 1)

The system of claim 1, wherein the electronic device comprises a graphics processing unit.

### Claim 4 (depends on 1)

The system of claim 1, wherein the electronic device comprises a system on a chip.

### Claim 5 (depends on 1)

The system of claim 1, wherein the plurality of parameters comprise a trip point temperature, a sample period, a limit, and a step size.

### Claim 6 (depends on 1)

The system of claim 1, wherein the plurality of parameters comprise a limit coefficient and an unlimit coefficient.

### Claim 7 (depends on 1)

The system of claim 1, wherein the machine-learning algorithm comprises a Bayesian Optimization with Gaussian Process.

### Claim 8 (depends on 1)

The system of claim 1, comprising monitoring the electronic device to obtain performance indicators of the electronic device while operating under the revised thermal policy configuration.

### Claim 9 (depends on 8)

The system of claim 8, wherein the performance indicators are obtained from a benchmark test used to evaluate the electronic device.

### Claim 10 (depends on 8)

The system of claim 8, wherein the performance indicators are used as constraints of the objective function.

### Claim 11 (depends on 1)

The system of claim 1, wherein the objective function comprises a scoring term, a temperature overshooting penalty term, and a saturation penalty term.

### Claim 12 (depends on 11)

The system of claim 11, wherein the scoring term represents a statistic result of benchmark scores of the electronic device.

### Claim 13 (depends on 11)

The system of claim 11, wherein the temperature overshooting penalty term represents an amount that the electronic device is over a threshold temperature over a period of time.

### Claim 14 (depends on 11)

The system of claim 11, wherein the saturation penalty term represents an amount of temperature fluctuation after the electronic device reaches temperature saturation.

### Claim 15

A method for thermal policy tuning on an electronic device, comprising:
accessing a thermal policy configuration comprising a plurality of parameters to control a thermal policy of the electronic device;
using the thermal policy configuration as input to a machine-learning algorithm, the machine-learning algorithm using an objective function to determine a revised thermal policy configuration; and
implementing the revised thermal policy configuration on the electronic device.

### Claim 16 (depends on 15)

The method of claim 15, wherein the machine-learning algorithm comprises a Bayesian Optimization with Gaussian Process.

### Claim 17 (depends on 15)

The method of claim 15, comprising monitoring the electronic device to obtain performance indicators of the electronic device while operating under the revised thermal policy configuration.

### Claim 18 (depends on 17)

The method of claim 17, wherein the performance indicators are obtained from a benchmark test used to evaluate the electronic device.

### Claim 19 (depends on 17)

The method of claim 17, wherein the performance indicators are used as constraints of the objective function.

### Claim 20

At least one machine-readable medium including instructions for thermal policy tuning on an electronic device, which when executed by a machine, cause the machine to perform operations comprising:
accessing a thermal policy configuration comprising a plurality of parameters to control a thermal policy of the electronic device;
using the thermal policy configuration as input to a machine-learning algorithm, the machine-learning algorithm using an objective function to determine a revised thermal policy configuration; and
implementing the revised thermal policy configuration on the electronic device.

### Claim 21 (depends on 20)

The machine-readable medium of claim 20, wherein the objective function comprises a scoring term, a temperature overshooting penalty term, and a saturation penalty term.

### Claim 22 (depends on 21)

The machine-readable medium of claim 21, wherein the scoring term represents a statistic result of benchmark scores of the electronic device.

### Claim 23 (depends on 21)

The machine-readable medium of claim 21, wherein the temperature overshooting penalty term represents an amount that the electronic device is over a threshold temperature over a period of time.

### Claim 24 (depends on 21)

The machine-readable medium of claim 21, wherein the saturation penalty term represents an amount of temperature fluctuation after the electronic device reaches temperature saturation.