Density-Based Area Recovery in EDA Floorplans
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Solution Overview
Problem
Conventional area recovery techniques in electronic design automation (EDA) are inefficient, leading to runtime bottlenecks in large chip designs due to unnecessary global timing updates and inefficient targeting of high-density areas.
Innovation Solution
Implementing region-based area recovery by overlaying a grid on the design floorplan, calculating region utilization, and performing area recovery only on regions with high utilization above a density threshold, while using a reverse-levelized cell order and limited timing propagation to optimize processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional area recovery techniques are applied to the entire design, then area recovery can be performed, but runtime bottlenecks occur due to unnecessary global timing updates
Solution Approach 1:
The design is divided into multiple regions using a grid overlay, where each grid cell represents a region. Area recovery is performed independently on each region based on its utilization, rather than applying global area recovery to the entire design. This segmentation eliminates unnecessary global timing updates and reduces computational overhead.
Solution Approach 2:
Different regions are treated differently based on their utilization characteristics. High-utilization regions (above density threshold) undergo area recovery, while low-utilization regions are skipped. This local differentiation ensures that area recovery efforts are concentrated where they are most needed, improving efficiency without sacrificing overall effectiveness.
2Manufacturing precision
If area recovery is performed on all regions, then comprehensive area optimization is achieved, but processor resources are wasted on low-utilization regions
Solution Approach 1:
Instead of applying area recovery to all regions, the technique applies partial action only to high-utilization regions that exceed a density threshold. This selective approach achieves sufficient area optimization for the overall design while avoiding the waste of processor resources on regions where area recovery would be ineffective.
Solution Approach 2:
The utilization parameter of each region is calculated and compared against a density threshold. Based on this parameter comparison, the system dynamically decides whether to apply area recovery to each region. This parameter-driven approach ensures that processor resources are allocated efficiently to regions where they will have the most impact.
3Reliability
If conventional area recovery techniques are used multiple times in the design flow, then design convergence can be facilitated, but total design time is disproportionately impacted by inefficiencies
Solution Approach 1:
By segmenting the design into regions and performing localized area recovery, each iteration of area recovery becomes significantly faster. This enables the technique to be applied multiple times in the design flow without disproportionately impacting total design time, thus maintaining design convergence benefits while reducing time overhead.
Solution Approach 2:
The region-based area recovery technique maintains continuous useful action by focusing computational effort only on high-utilization regions throughout multiple design flow iterations. This eliminates wasted computational cycles on low-utilization regions, allowing the technique to be repeated efficiently multiple times to achieve design convergence.
Data Source
AI summary
Some embodiments provide techniques and systems for improving the efficiency of area recovery in an electronic design automation (EDA) flow. During operation, the system determines a utilization of a region from a set of regions in a design floorplan. Next, the system performs area recovery (e.g., by using a processor) on the region based at least on the utilization. Specifically, the system can overlay the design floorplan with a grid, wherein the grid comprises a set of grid cells and uses the grid cells as the set of regions. The grid can be associated with a predetermined number of rows and a predetermined number of columns. The system can determine the utilization of the region by calculating the utilization as a cell area of the region divided by a placement area of the region. The utilization can be incrementally calculated during the creation and optimization of the design.