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

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 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.