17958116. SYSTEMS AND METHODS FOR GENERATING REMEDY RECOMMENDATIONS FOR POWER AND PERFORMANCE ISSUES WITHIN SEMICONDUCTOR SOFTWARE AND HARDWARE simplified abstract (ADVANCED MICRO DEVICES, INC.)
Contents
- 1 SYSTEMS AND METHODS FOR GENERATING REMEDY RECOMMENDATIONS FOR POWER AND PERFORMANCE ISSUES WITHIN SEMICONDUCTOR SOFTWARE AND HARDWARE
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 SYSTEMS AND METHODS FOR GENERATING REMEDY RECOMMENDATIONS FOR POWER AND PERFORMANCE ISSUES WITHIN SEMICONDUCTOR SOFTWARE AND HARDWARE - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Original Abstract Submitted
SYSTEMS AND METHODS FOR GENERATING REMEDY RECOMMENDATIONS FOR POWER AND PERFORMANCE ISSUES WITHIN SEMICONDUCTOR SOFTWARE AND HARDWARE
Organization Name
Inventor(s)
Mohammad Hamed Mousazadeh of Markham (CA)
Phillippe John Louis Yu 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 17958116 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 a rule-based model to telemetry data to identify root causes and utilizing a machine learning model to analyze complex failure patterns and generate specific remedy recommendations for the identified failure and client computing device.
- Rule-based model applied to telemetry data
- Machine learning model used to analyze failure patterns
- Specific remedy recommendations generated for identified failures
- Focus on power and performance issues in semiconductor software and hardware
Potential Applications
This technology could be applied in various industries such as:
- Semiconductor manufacturing
- Computer hardware development
- Software development for electronic devices
Problems Solved
- Efficient identification of power and performance issues
- Tailored remedy recommendations for specific failures
- Improved overall performance of client computing devices
Benefits
- Enhanced troubleshooting process
- Increased reliability of semiconductor software and hardware
- Cost-effective solutions for power and performance issues
Potential Commercial Applications
Optimizing power and performance in:
- Mobile devices
- Servers and data centers
- Automotive electronics
Possible Prior Art
One possible prior art could be the use of rule-based models and machine learning in fault detection and analysis in various industries, including semiconductor manufacturing and computer hardware development.
What are the specific telemetry data used in the analysis process?
The specific telemetry data used in the analysis process are not detailed in the abstract. It would be beneficial to know the types of telemetry data collected and how they are utilized in generating remedy recommendations.
How does the machine learning model differentiate between different failure patterns?
The abstract mentions that the machine learning model analyzes deep and complex failure patterns, but it does not specify how the model distinguishes between different types of failure patterns. Understanding this process would provide insight into the accuracy and effectiveness of the remedy recommendations.
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.
