Integrated Circuit Design Search with Multi-Metric Reward Aggregation
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Solution Overview
Problem
Designing integrated circuits is challenging due to the vast and complex design-search spaces, where each design choice impacts the quality of the final chip, making it difficult to optimize multiple component metrics efficiently.
Innovation Solution
A method and apparatus that aggregates multiple component metrics into a single reward function (AOV) using machine learning, allowing designers to specify target values, baselines, and unacceptable values, and adjusts component input values to optimize the AOV, thereby ranking design implementations effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If the entire design-search space is searched to find optimal design choices, then the quality of the final chip is improved, but the time and computational resources required become prohibitively large
Solution Approach 1:
The patent segments the design-search space into multiple smaller subspaces by dividing component metrics into separate dimensions. Each dimension represents a specific component metric that can be optimized independently, allowing the search to proceed through structured stages rather than exhaustively searching the entire space at once.
Solution Approach 2:
The patent transforms the multi-dimensional design-search space into a unified one-dimensional reward function (AOV). By aggregating multiple component metrics into a single scalar value, the system converts a complex multi-objective optimization problem into a simpler single-objective problem that can be efficiently searched.
2Reliability
If multiple component metrics are optimized simultaneously, then the overall design performance is improved, but the complexity of the optimization process increases
Solution Approach 1:
The patent merges multiple component metrics into a single Aggregate Design Score (AOV) reward function. This combination allows the optimization system to handle multiple objectives simultaneously by treating them as a unified metric, reducing the apparent complexity while maintaining comprehensive performance optimization.
Solution Approach 2:
The reward function acts as an intermediary between multiple component metrics and the optimization algorithm. Instead of directly optimizing multiple conflicting metrics, the system uses the reward function as a mediator that translates multiple objectives into a single guiding signal for the search process.
3Adaptability or versatility
If traditional design optimization methods are used, then individual component metrics can be adjusted, but it is difficult to evaluate tradeoffs between multiple metrics efficiently
Solution Approach 1:
The patent changes the parameter representation by transforming multiple component metric values into a single Aggregate Design Score. This parameter transformation enables efficient comparison and evaluation of different design implementations by providing a unified metric that captures tradeoffs across all component metrics.
Data Source
AI summary
A method and apparatus for aggregating multiple component metrics into a single reward function output value (AOV) is disclosed. The method comprises determining at least one component metric value to be used to determine the AOV. In addition, the method comprises determining at least one component metric target value to strive to achieve. Furthermore, the method comprises determining at least one baseline value to use when determining the AOV. Still further, the method comprises determining unacceptable values for particular component metrics. The method also comprises combining at least one component metric value into a single AOV and optimizing component input values to achieve the best AOV.