Memory Macro Alignment in Chip Floor Planning With RL
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Solution Overview
Problem
Modern chip design faces challenges in floor planning, particularly in aligning memory macros, which can lead to difficulties in interpreting placements and violating design assumptions, resulting in inefficiencies and increased complexity.
Innovation Solution
A Markov decision process (MDP) and deep reinforcement learning (RL) agent are used to determine optimal memory macro placements, incorporating alignment as a regularization cost and controlling tradeoffs through a tunable alignment parameter, with a placement proxy cost based on weighted alignment, density, and congestion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated chip floor planning tools are used to find efficient placements of memory macros, then power consumption and area are reduced, but the placements are not necessarily aligned causing difficulties in interpreting and debugging
Solution Approach 1:
The patent introduces an alignment parameter that controls the tradeoff between placement efficiency and alignment. By adjusting this parameter, the system can optimize placements to achieve both efficiency and alignment, resolving the contradiction between productivity and ease of operation.
Solution Approach 2:
The patent employs reinforcement learning where the alignment of placements is evaluated and fed back into the learning process. This feedback mechanism allows the system to learn from alignment outcomes and improve future placements, simultaneously achieving efficiency and interpretability.
2Productivity
If automated chip floor planning tools are used to find efficient placements of memory macros, then power consumption and area are reduced, but downstream design assumptions are violated
Solution Approach 1:
The alignment parameter serves as a control mechanism that ensures placements meet downstream design assumptions. By tuning this parameter, the system maintains reliability of design constraints while preserving the productivity benefits of automated floor planning.
Solution Approach 2:
The patent proactively enforces alignment constraints during the floor planning stage to prevent violations of downstream design assumptions. This preliminary action avoids the need for costly corrections in later design stages, maintaining both efficiency and reliability.
3Manufacturing precision
If alignment is enforced as a constraint in floor planning, then placement alignment improves, but the optimization space is reduced
Solution Approach 1:
Instead of enforcing rigid alignment constraints that reduce optimization space, the patent uses a soft alignment parameter that guides the optimization process. This approach maintains manufacturing precision while preserving optimization flexibility by allowing gradual alignment improvement rather than hard constraints.
Solution Approach 2:
The alignment parameter is dynamically adjusted during the reinforcement learning process, allowing the system to adaptively balance alignment requirements with optimization space. This dynamic approach prevents premature convergence and maintains device complexity at manageable levels.
Data Source
AI summary
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.