View source for 17958116. SYSTEMS AND METHODS FOR GENERATING REMEDY RECOMMENDATIONS FOR POWER AND PERFORMANCE ISSUES WITHIN SEMICONDUCTOR SOFTWARE AND HARDWARE simplified abstract (ATI TECHNOLOGIES ULC)

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


==Organization Name==

[[:Category:ATI TECHNOLOGIES ULC|ATI TECHNOLOGIES ULC]]


[[Category:ATI TECHNOLOGIES ULC]]

==Inventor(s)==

[[:Category:Mohammad Hamed Mousazadeh of Markham (CA)|Mohammad Hamed Mousazadeh of Markham (CA)]][[Category:Mohammad Hamed Mousazadeh of Markham (CA)]]

[[:Category:Arpit Patel of Markham (CA)|Arpit Patel of Markham (CA)]][[Category:Arpit Patel of Markham (CA)]]

[[:Category:Gabor Sines of Markham (CA)|Gabor Sines of Markham (CA)]][[Category:Gabor Sines of Markham (CA)]]

[[:Category:Omer Irshad of Markham (CA)|Omer Irshad of Markham (CA)]][[Category:Omer Irshad of Markham (CA)]]

[[:Category:Phillippe John Louis Yu of Markham (CA)|Phillippe John Louis Yu of Markham (CA)]][[Category:Phillippe John Louis Yu of Markham (CA)]]

[[:Category:Zongjie Yan of Markham (CA)|Zongjie Yan of Markham (CA)]][[Category:Zongjie Yan of Markham (CA)]]

[[:Category:Ian Charles Colbert of San Diego CA (US)|Ian Charles Colbert of San Diego CA (US)]][[Category: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.

* Rule-based model applied to telemetry data
* Machine learning model used to analyze complex failure patterns
* Specific remedy recommendations generated for identified failures

== Potential Applications ==
This technology could be applied in various industries such as semiconductor manufacturing, computer hardware development, and software optimization.

== Problems Solved ==
This technology addresses power and performance issues within semiconductor software and hardware, providing specific remedy recommendations to improve overall efficiency and functionality.

== Benefits ==
The benefits of this technology include enhanced troubleshooting capabilities, improved performance optimization, and increased reliability of semiconductor devices.

== Potential Commercial Applications ==
Potential commercial applications of this technology include semiconductor companies, computer hardware manufacturers, and software development firms looking to enhance the efficiency and performance of their products.

== Possible Prior Art ==
One possible prior art could be existing systems that utilize telemetry data for troubleshooting power and performance issues in semiconductor devices. Another could be machine learning models used for analyzing complex failure patterns in hardware and software systems.

== Unanswered Questions ==

=== How does this technology compare to existing solutions in terms of accuracy and efficiency? ===
This article does not provide a direct comparison between this technology and existing solutions in terms of accuracy and efficiency.

=== What are the potential limitations or challenges in implementing this technology on a large scale? ===
This article does not address the potential limitations or challenges in implementing this technology on a large scale.


==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.



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[[Category:G06K9/62]]
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