Dynamic Weighting for IC Clock Tree Clustering
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Solution Overview
Problem
Conventional clock tree synthesis techniques for integrated circuits result in suboptimal clustering solutions due to reliance on static weights, leading to increased clock buffer count, area, and power consumption, as they only refine failing clusters and not barely passing ones.
Innovation Solution
A dynamic weighting scheme is employed in the clock tree synthesis tool to evaluate and refine both failing and barely passing clusters, allowing for the optimization of clock tree construction by dynamically determining the importance of design metrics and improving clock buffer count, area, and power consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If static weights are used in conventional clock tree synthesis, then the clustering process is simple and fast, but the clock buffer count, area, and power consumption increase
Solution Approach 1:
The patent applies dynamics by transitioning from static weights to dynamic weights that adapt during the clustering process. The weight for each design metric (such as skew, transition, and capacitance) is updated iteratively based on the current clustering state and constraint satisfaction levels, allowing the algorithm to respond to changing conditions and optimize buffer placement dynamically.
Solution Approach 2:
The patent changes the parameter of weights from fixed static values to variable dynamic values. The dynamic weight for each metric is calculated based on the ratio of current metric value to target metric value, and the algorithm adjusts these weights across iterations to prioritize metrics that are closer to their constraints, thereby reducing the number of buffers needed.
2Device complexity
If static weights are used in conventional clock tree synthesis, then the algorithm is computationally simple, but the clock tree area increases
Solution Approach 1:
The algorithm introduces dynamic weight adjustment mechanisms that adapt during each iteration of the clustering process. This dynamic approach allows the algorithm to focus computational effort on metrics that are most critical at each stage, achieving better area optimization without requiring excessively complex static algorithms.
Solution Approach 2:
The patent implements feedback by continuously evaluating the current clustering solution against target constraints and using this information to adjust weights for the next iteration. The dynamic weight calculation incorporates feedback from metric violations and constraint satisfaction levels, enabling the algorithm to learn and improve its clustering decisions across iterations.
3Ease of manufacture
If static weights are used in conventional clock tree synthesis, then the implementation is straightforward, but the power consumption increases
Solution Approach 1:
The patent changes the implementation from fixed parameter weights to dynamically changing weights that adapt based on constraint satisfaction. This parameter change enables the algorithm to identify and optimize high-power-consuming clusters more effectively, reducing overall power consumption while maintaining a relatively simple iterative implementation structure.
Solution Approach 2:
The algorithm applies partial action by focusing refinement efforts on specific clusters that are close to violating constraints rather than uniformly processing all clusters. The dynamic weighting allows the algorithm to concentrate computational resources on the most critical areas, achieving power optimization without requiring exhaustive processing of the entire clock tree.
4Productivity
If only failing clusters are refined, then the processing scope is limited and fast, but barely passing clusters are not optimized
Solution Approach 1:
The patent extends the refinement scope dynamically by using weight-based prioritization rather than fixed threshold-based selection. Clusters are refined based on their dynamic weight scores that reflect how close they are to constraint violations, allowing the algorithm to proactively optimize barely passing clusters before they become failing, thereby improving overall optimization quality.
Solution Approach 2:
The algorithm performs preliminary action by identifying and refining clusters that are close to violating constraints before they actually fail. The dynamic weight calculation detects clusters that are approaching constraint boundaries and prioritizes their refinement in advance, preventing future violations and achieving better overall clustering quality.
Data Source
AI summary
Aspects of the present disclosure address systems and methods for local cluster refinement for integrated circuit (IC) designs using a dynamic weighting scheme. Initial cluster definitions are accessed. The initial cluster definitions define a plurality of clusters where each cluster includes a plurality of pins. Each cluster is evaluated with respect to one or more design rule constraints. Based on the evaluation, clusters are identified from the plurality of clusters. A set of refinement candidates are generated based on the one or more clusters. A scoring function that employs a dynamic weighting scheme is used to determine a refinement quality score for each refinement candidate in the set of candidates and one or more refinement candidates are selected from among the set of refinement candidates based on respective refinement quality scores. A refined clustering solution is generated based on the selected refinement candidates.