# US 11,906,580 B2 · Automated overclocking using a prediction model

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- Number: US 11,906,580 B2
- Document ID: US11906580B2
- Status: Granted
- Kind: B2
- Application number: 17/289,057
- Filed: 2018-12-07
- Published: 2021-12-30
- Granted: 2024-02-20
- Earliest claimed: 2018-12-07
- Assignees: Hewlett-Packard Development Company, L.P.
- Applicants: Hewlett-Packard Development Company, L.P.
- Inventors: Suketu Partiwala; Hiren Bhatt; Dhruv Jain; Chihao Lo; Arnaud Froment
- Web page: https://patentgrep.com/patent/US11906580B2

## CPC

- G01R 31/30: Marginal testing, e.g. by varying supply voltage (testing computers during standby operation or idle time G06F11/22)
- G01R 31/3172: {Optimisation aspects, e.g. using functional pin as test pin, pin multiplexing}
- G06F 1/08: Clock generators with changeable or programmable clock frequency
- G06F 1/206: {comprising thermal management}
- G06F 1/324: by lowering clock frequency
- G06F 11/0721: {within a central processing unit [CPU]}
- G06F 11/0754: {by exceeding limits}
- G06F 11/3024: {where the computing system component is a central processing unit [CPU]}
- G06F 11/3055: {Monitoring arrangements for monitoring the status of the computing system or of the computing system component, e.g. monitoring if the computing system is on, off, available, not available (error or fault processing without redundancy G06F11/0703; error detection or correction by redundancy in data representation G06F11/08; error detection or correction of the data by redundancy in operations G06F11/14; error detection or correction by redundancy in hardware G06F11/16)}
- G06F 11/3089: {Monitoring arrangements determined by the means or processing involved in sensing the monitored data, e.g. interfaces, connectors, sensors, probes, agents (software debugging using additional hardware using a specific debug interface G06F11/3656; performance evaluation by tracing or monitoring G06F11/3466)}
- G06F 11/3428: {Benchmarking}
- G06F 11/3476: {Data logging (G06F11/14, G06F11/2205 take precedence)}
- G06F 30/3312: Timing analysis
- G01R 31/2872: {related to electrical or environmental aspects, e.g. temperature, humidity, vibration, nuclear radiation}
- G06F 2115/10: Processors
- G06F 2119/12: Timing analysis or timing optimisation
- Y02D 10/00: Energy efficient computing, e.g. low power processors, power management or thermal management
- G01R: MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES (indicating correct tuning of resonant circuits H03J3/12)
- G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N)
- Y02D: CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE

## Abstract

A system, a method, and a machine-readable medium for overclocking a computer system is provided. An example of a method for overclocking a computer system includes predicting a stable operating frequency for a central processing unit (CPU) in a target system based, at least in part, on a model generated from data collected for a test system. An operating frequency for the CPU is adjusted to the stable operating frequency. A benchmark test is run to confirm that the CPU is operating within limits.

## Claims

### Claim 1

A method for overclocking a computer system, comprising:
predicting a stable operating frequency for a central processing unit (CPU) in a target system based, at least in part, on an output of a prediction model generated from data collected for a test system;
adjusting an operating frequency for the CPU to the stable operating frequency; and
running a benchmark test to confirm that the CPU is operating within limits.

### Claim 2 (depends on 1)

The method of claim 1, comprising:
running the benchmark test on the test system to generate a data set; and
creating the prediction model using the data set to correlate input parameters to output parameters in the data set for the test system.

### Claim 3 (depends on 2)

The method of claim 2, comprising:
determining a range of input parameters for the test system;
iterating through the range of input parameters; and
running the benchmark test at each combination of input parameters while logging the output parameters.

### Claim 4 (depends on 1)

The method of claim 1, comprising:
running the benchmark test on multiple test systems to generate a data set;
creating an average system configuration; and
creating the prediction model using the data and the average system configuration to correlate output parameters to input parameters in the data set.

### Claim 5 (depends on 1)

The method of claim 1, comprising collecting the data for the test system by:
identifying input parameters and ranges in a configuration of the test system;
logging the configuration of the test system;
configuring the test system with the input parameters;
running the benchmark test to collect output parameters associated with the input parameters; and
logging the output parameters for the test system.

### Claim 6 (depends on 5)

The method of claim 5, wherein the input parameters comprise at least one of CPU frequency, CPU voltage, cache frequency, or cache voltage.

### Claim 7 (depends on 5)

The method of claim 5, wherein the output parameters comprise at least one of CPU temperature, CPU core frequency, memory utilization, thermal throttling percentage, memory frequency, or CPU fan speed.

### Claim 8 (depends on 5)

The method of claim 5, comprising performing a statistical regression to create the prediction model correlating the input parameters and the output parameters for the test system.

### Claim 9 (depends on 8)

The method of claim 8, comprising determining a delta between the target system and the test system.

### Claim 10 (depends on 9)

The method of claim 9, comprising generating a set of input parameters for the target system based, at least in part, on the delta between the target system and the test system.

### Claim 11 (depends on 1)

The method of claim 1, wherein the prediction model comprises a neural network.

### Claim 12

An overclocking system comprising:
a processor to:
generate a prediction model based on a benchmark test executed upon a test computer system, the test computer system comprising a first central processing unit (CPU) operating at a first CPU frequency and a first CPU voltage;
predict a stable operating frequency for a target computer system by using the prediction model and input parameters received from the target computer system, the target computer system comprising a second CPU operating at a second CPU frequency and a second CPU voltage; and
adjust the second CPU frequency of the target computer system based on the predicted stable operating frequency.

### Claim 13 (depends on 12)

The overclocking system of claim 12, wherein the prediction model correlates the input parameters with output parameters associated with the benchmark test.

### Claim 14 (depends on 13)

The overclocking system of claim 13, wherein the prediction model comprises a statistical correlation between the output parameters and the input parameters associated with the benchmark test.

### Claim 15 (depends on 14)

The overclocking system of claim 14, wherein the statistical correlation between the output parameters and the input parameters is determined responsive to performing a statistical regression.

### Claim 16 (depends on 15)

The overclocking system of claim 15, wherein the prediction model comprises a neural network.

### Claim 17

A non-transitory machine-readable medium comprising instructions that, in response to being executed on a computing device, cause the computing device to:
predict a stable operating frequency for a central processing unit (CPU) in the computing device based, at least in part, on a prediction model generated from data collected for a second CPU in a second computing device; and
adjust an operating frequency for the CPU to the stable operating frequency.

### Claim 18 (depends on 17)

The machine-readable medium of claim 17, comprising instructions that, in response to being executed on the computing device, cause the computing device to:
identify a range for an input parameter associated with the second CPU;
run a benchmark test on the second CPU at the input parameter; and
log an output parameter associated with the input parameter based on the benchmark test;
increment the input parameter; and
iterate through the range for the input parameter to complete the benchmark test.

### Claim 19 (depends on 17)

The machine-readable medium of claim 17, comprising instructions that, in response to being executed on the computing device, cause the computing device to generate the prediction model by performing a statistical regression using the data collected for the second CPU in the second computing device.

### Claim 20 (depends on 19)

The machine-readable medium of claim 19, wherein the prediction model comprises a neural network.