View source for 17875502. CONFIGURING COMPUTE EXPRESS LINK (CXL) ATTRIBUTES FOR BEST KNOWN CONFIGURATION simplified abstract (Dell Products L.P.)

=CONFIGURING COMPUTE EXPRESS LINK (CXL) ATTRIBUTES FOR BEST KNOWN CONFIGURATION=


==Organization Name==

[[:Category:Dell Products L.P.|Dell Products L.P.]]


[[Category:Dell Products L.P.]]

==Inventor(s)==

[[:Category:Shekar Babu Suryanarayana of Bangalore (IN)|Shekar Babu Suryanarayana of Bangalore (IN)]][[Category:Shekar Babu Suryanarayana of Bangalore (IN)]]

[[:Category:Sumanth Vidyadhara of Bangalore (IN)|Sumanth Vidyadhara of Bangalore (IN)]][[Category:Sumanth Vidyadhara of Bangalore (IN)]]

==CONFIGURING COMPUTE EXPRESS LINK (CXL) ATTRIBUTES FOR BEST KNOWN CONFIGURATION - A simplified explanation of the abstract==


This abstract first appeared for US patent application 17875502 titled 'CONFIGURING COMPUTE EXPRESS LINK (CXL) ATTRIBUTES FOR BEST KNOWN CONFIGURATION

==Simplified Explanation==

The abstract of the patent application describes a method for updating firmware in a computing system. The method involves storing a firmware update in a memory area during boot time, performing update operations after a reboot, and saving information about attribute changes in a non-volatile store. The method also includes obtaining recommendations from a machine learning inference engine.

* The method involves updating firmware in a computing system after a reboot.
* The firmware update is stored in a memory area during boot time.
* The update operations include mapping a runtime memory area to a non-volatile store, extracting the firmware update, and creating objects for dynamic configuration of parameters.
* Current attributes are compared with stored attributes to identify changes, and the changes are saved in the non-volatile store.
* After a subsequent reboot, existing attributes are identified from the non-volatile store, and dynamic attributes are applied if a flag is set.
* Recommendations for dynamic attributes are obtained from a machine learning inference engine.

Potential applications of this technology:
* This technology can be used in various computing systems, such as servers, data centers, and embedded systems, to update firmware efficiently.
* It can be particularly useful in systems that require frequent firmware updates or dynamic configuration of parameters.

Problems solved by this technology:
* This method simplifies the process of updating firmware by automating the update operations and dynamically configuring parameters.
* It ensures that attribute changes are properly identified and saved, reducing the risk of configuration errors or inconsistencies.

Benefits of this technology:
* The method allows for efficient and reliable firmware updates, reducing downtime and improving system performance.
* Dynamic configuration of parameters based on recommendations from a machine learning inference engine can optimize system settings for better performance and energy efficiency.
* The method provides a systematic approach to managing firmware updates and attribute changes, ensuring consistency and reliability in the computing system.


==Original Abstract Submitted==

Disclosed methods may push a capsule update including a best known configuration—compute express link (BKC-CXL) firmware update to a boot time memory area. Following a platform reboot, BKC-CXL firmware update operations are performed. The update operations include mapping a BKC-CXL runtime memory area to a non-volatile BKC store, identifying current CXL attributes from the runtime memory area, extracting the firmware update, creating one or more BKC-CXL objects from the firmware update to enable dynamic configuration of CXL parameters, comparing current CXL attributes with stored CXL attributes to identify CXL attribute changes, and saving information indicative of the CXL attribute changes back to the non-volatile BKC store. Following a subsequent reboot, regular boot path operations are performed, including identifying existing CXL attributes indicated in the non-volatile BKC store and responsive to detecting a flag set to apply vendor pushed dynamic CXL attributes, obtaining BKC-CXL recommendations from an ML inference engine.



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