Advanced micro devices, inc. (20240111620). SYSTEMS AND METHODS FOR GENERATING REMEDY RECOMMENDATIONS FOR POWER AND PERFORMANCE ISSUES WITHIN SEMICONDUCTOR SOFTWARE AND HARDWARE simplified abstract

SYSTEMS AND METHODS FOR GENERATING REMEDY RECOMMENDATIONS FOR POWER AND PERFORMANCE ISSUES WITHIN SEMICONDUCTOR SOFTWARE AND HARDWARE

Organization Name

advanced micro devices, inc.

Inventor(s)

Mohammad Hamed Mousazadeh of Markham (CA)

Arpit Patel of Markham (CA)

Gabor Sines of Markham (CA)

Omer Irshad of Markham (CA)

Phillippe John Louis Yu of Markham (CA)

Zongjie Yan of Markham (CA)

Ian Charles Colbert of San Diego CA (US)

SYSTEMS AND METHODS FOR GENERATING REMEDY RECOMMENDATIONS FOR POWER AND PERFORMANCE ISSUES WITHIN SEMICONDUCTOR SOFTWARE AND HARDWARE - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240111620 titled 'SYSTEMS AND METHODS FOR GENERATING REMEDY RECOMMENDATIONS FOR POWER AND PERFORMANCE ISSUES WITHIN SEMICONDUCTOR SOFTWARE AND HARDWARE

Simplified Explanation

The disclosed computer-implemented method involves generating remedy recommendations for power and performance issues within semiconductor software and hardware. This includes applying rule-based and machine learning models to telemetry data to identify and address failures specific to the client computing device.

• Rule-based and machine learning models are used to analyze telemetry data for power and performance issues in semiconductor software and hardware.
• The method generates rule-based root-cause outputs and telemetry-based unknown outputs to identify failure patterns.
• By applying a root-cause machine learning model to the telemetry-based unknown outputs, specific remedy recommendations are generated for the identified failures and client computing devices.

Potential Applications

This technology could be applied in:

• Semiconductor manufacturing
• Computer hardware troubleshooting
• Software development for optimized performance

Problems Solved

This technology helps in:

• Identifying root causes of power and performance issues
• Providing specific remedy recommendations for failures
• Improving overall efficiency and performance of semiconductor software and hardware

Benefits

The benefits of this technology include:

• Faster identification and resolution of power and performance issues
• Tailored remedy recommendations for specific failures
• Enhanced performance and reliability of semiconductor devices

Potential Commercial Applications

This technology could be commercially applied in:

• Semiconductor companies
• IT support services
• Computer hardware manufacturers

Possible Prior Art

One possible prior art could be the use of rule-based models in analyzing telemetry data for identifying failures in computing devices. Additionally, machine learning models have been used in various industries for pattern recognition and analysis.

What are the limitations of this technology in real-world applications?

The limitations of this technology in real-world applications may include:

• The need for extensive and accurate telemetry data for effective analysis
• Potential challenges in integrating the remedy recommendations into existing systems

How does this technology compare to traditional methods of identifying and resolving power and performance issues in semiconductor devices?

This technology offers a more advanced and data-driven approach compared to traditional methods, allowing for more accurate and specific remedy recommendations based on deep analysis of telemetry data.

Original Abstract Submitted

the disclosed computer-implemented method for generating remedy recommendations for power and performance issues within semiconductor software and hardware. for example, the disclosed systems and methods can apply a rule-based model to telemetry data to generate rule-based root-cause outputs as well as telemetry-based unknown outputs. the disclosed systems and methods can further apply a root-cause machine learning model to the telemetry-based unknown outputs to analyze deep and complex failure patterns with the telemetry-based unknown outputs to ultimately generate one or more root-cause remedy recommendations that are specific to the identified failure and the client computing device that is experiencing that failure.