View source for 17890370. Alignment Cost for Integrated Circuit Placement simplified abstract (Google LLC)

=Alignment Cost for Integrated Circuit Placement=


==Organization Name==

[[:Category:Google LLC|Google LLC]]


[[Category:Google LLC]]

==Inventor(s)==

[[:Category:Ebrahim Mohammadgholi Songhori of Sunnyvale CA (US)|Ebrahim Mohammadgholi Songhori of Sunnyvale CA (US)]][[Category:Ebrahim Mohammadgholi Songhori of Sunnyvale CA (US)]]

[[:Category:Shen Wang of Sunnyvale CA (US)|Shen Wang of Sunnyvale CA (US)]][[Category:Shen Wang of Sunnyvale CA (US)]]

[[:Category:Azalia Mirhoseini of Mountain View CA (US)|Azalia Mirhoseini of Mountain View CA (US)]][[Category:Azalia Mirhoseini of Mountain View CA (US)]]

[[:Category:Anna Goldie of San Francisco CA (US)|Anna Goldie of San Francisco CA (US)]][[Category:Anna Goldie of San Francisco CA (US)]]

[[:Category:Roger Carpenter of Novato CA (US)|Roger Carpenter of Novato CA (US)]][[Category:Roger Carpenter of Novato CA (US)]]

[[:Category:Wenjie Jiang of Fremont CA (US)|Wenjie Jiang of Fremont CA (US)]][[Category:Wenjie Jiang of Fremont CA (US)]]

[[:Category:Young-Joon Lee of Saratoga CA (US)|Young-Joon Lee of Saratoga CA (US)]][[Category:Young-Joon Lee of Saratoga CA (US)]]

[[:Category:James Laudon of Madison WI (US)|James Laudon of Madison WI (US)]][[Category:James Laudon of Madison WI (US)]]

==Alignment Cost for Integrated Circuit Placement - A simplified explanation of the abstract==


This abstract first appeared for US patent application 17890370 titled 'Alignment Cost for Integrated Circuit Placement

==Simplified Explanation==

The patent application focuses on using deep reinforcement learning to automatically determine floor planning in chips, specifically considering memory macro alignment as a factor. The RL agent is trained to optimize the placement of memory macros by incorporating memory macro alignment as a regularization cost in the placement objective.

* Memory macro alignment is a key factor in determining optimal floor planning in chips.
* Deep reinforcement learning is utilized to train an agent to optimize memory macro placements.
* Memory macro alignment is included as a regularization cost in the placement objective.
* The tradeoffs between placement objective and macro alignment can be controlled by a tunable alignment parameter.

== Potential Applications ==
The technology can be applied in the semiconductor industry for designing efficient chip layouts with optimized memory macro placements.

== Problems Solved ==
1. Automating the process of determining floor planning in chips.
2. Improving the efficiency of memory macro alignment in chip design.

== Benefits ==
1. Enhanced chip performance through optimized memory macro placements.
2. Reduction in design time and costs associated with chip layout planning.

== Potential Commercial Applications ==
Optimizing memory macro placements in chip design for improved performance and cost-efficiency.

== Possible Prior Art ==
There may be prior art related to using reinforcement learning for chip design optimization, but specific examples are not provided in the abstract.

== Unanswered Questions ==
=== How does the tunable alignment parameter affect the tradeoffs between placement objective and macro alignment in chip design optimization? ===
The abstract mentions a tunable alignment parameter, but it does not elaborate on how exactly it influences the balance between placement objective and macro alignment.

=== What specific techniques are used to incorporate memory macro alignment as a regularization cost in the placement objective? ===
While the abstract mentions including memory macro alignment as a regularization cost, it does not detail the specific methods or algorithms used to achieve this in the chip design process.


==Original Abstract Submitted==

Aspects of the disclosure are directed to automatically determining floor planning in chips, which factors in memory macro alignment. A deep reinforcement learning (RL) agent can be trained to determine optimal placements for the memory macros, where memory macro alignment can be included as a regularization cost to be added to the placement objective as a RL reward. Tradeoffs between the placement objective and alignment of macros can be controlled by a tunable alignment parameter.



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