Google llc (20240095424). Alignment Cost for Integrated Circuit Placement simplified abstract

Alignment Cost for Integrated Circuit Placement

Organization Name

google llc

Inventor(s)

Ebrahim Mohammadgholi Songhori of Sunnyvale CA (US)

Shen Wang of Sunnyvale CA (US)

Azalia Mirhoseini of Mountain View CA (US)

Anna Goldie of San Francisco CA (US)

Roger Carpenter of Novato CA (US)

Wenjie Jiang of Fremont CA (US)

Young-Joon Lee of Saratoga CA (US)

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 20240095424 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. By training a deep RL agent, optimal placements for memory macros can be determined, with memory macro alignment included as a regularization cost. The tradeoffs between placement objectives and macro alignment can be adjusted using a tunable alignment parameter.

• Explanation of the patent:

- Utilizes deep reinforcement learning for chip floor planning - Considers memory macro alignment in placement optimization - Includes memory macro alignment as a regularization cost - Allows control over tradeoffs between placement objectives and macro alignment

Potential Applications

The technology can be applied in: - Semiconductor industry for chip design optimization - Electronics manufacturing for improving memory macro alignment

Problems Solved

- Streamlines the chip floor planning process - Enhances memory macro alignment accuracy - Provides a systematic approach to optimizing chip design

Benefits

- Increased efficiency in chip design - Improved performance of memory macros - Cost-effective solution for chip manufacturers

Potential Commercial Applications

Optimizing Memory Macro Alignment in Chip Design: A Cost-Effective Solution

Possible Prior Art

Prior art in chip design and optimization techniques may exist, but specific examples are not provided in this context.

How does this technology compare to traditional chip floor planning methods?

The article does not provide a direct comparison between this technology and traditional chip floor planning methods.

What are the potential limitations of using deep reinforcement learning for chip floor planning?

The article does not discuss any potential limitations or challenges associated with using deep reinforcement learning for chip floor planning.

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.