Dynamic Scenario Reduction for Multi-Circuit Optimization
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Solution Overview
Problem
The increasing complexity and miniaturization of integrated circuits lead to a rapid increase in the number of scenarios that circuit designs must be optimized for, making traditional optimization methods computationally intensive and impractical due to high run-time and memory overhead.
Innovation Solution
The approach involves determining margin values for each gate in multiple scenarios to identify a subset of essential scenarios, optimizing the circuit design on a bucket-by-bucket basis, and updating circuit information only in these essential scenarios, thereby reducing the computational load.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If circuit design is optimized across all scenarios to ensure functional and performance requirements are met, then reliability is improved, but computational complexity and run-time increase significantly
Solution Approach 1:
The patent segments the complete set of scenarios into a subset of essential scenarios that are sufficient for achieving optimal implementation results. By dividing the optimization problem into a manageable subset rather than processing all scenarios, the system reduces computational complexity while maintaining reliability requirements.
Solution Approach 2:
The patent changes the parameter of scenario selection by using margin values to identify which scenarios are truly essential for optimization. This parameter change allows the system to focus computational resources on critical scenarios rather than uniformly processing all scenarios, thereby reducing overall computational complexity.
2Adaptability or versatility
If the number of scenarios for optimization is increased to cover all operating conditions, then adaptability is improved, but memory overhead and run-time become impractical
Solution Approach 1:
The patent extracts only the essential scenarios from the complete set of operating conditions using margin value analysis. This extraction process removes non-essential scenarios that do not contribute significantly to optimization quality, thereby reducing memory overhead and run-time while preserving adaptability across critical operating conditions.
3Manufacturing precision
If traditional optimization methods are applied to all scenarios, then manufacturing precision is improved, but productivity decreases due to computational intensity
Solution Approach 1:
The patent applies partial action by optimizing only the essential scenarios rather than all scenarios. This partial approach achieves sufficient optimization quality for manufacturing precision while dramatically improving productivity by avoiding unnecessary computational work on non-essential scenarios.
Data Source
AI summary
Some embodiments of the present invention provide techniques and systems for performing aggressive and dynamic scenario reduction during different phases of optimization. Specifically, essential scenarios at gates and timing end-points can be identified and then used during the dynamic scenario reduction process. In some embodiments, margin values associated with various constraints can be used to determine the set of essential scenarios to account for constrained objects that are near critical in addition to the constrained objects that are the worst violators. In some embodiments, at any point during the optimization process, only the set of essential scenarios are kept active, thereby substantially reducing runtime and memory requirements without compromising on the quality of results.