IC Metric Prediction Models for Faster Design Optimization
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Solution Overview
Problem
Conventional integrated circuit (IC) design tools require extensive computation time and resources, often taking hours to days for synthesis, and provide limited availability, obscuring the relationship between design specifications and performance metrics, making iterative design optimization cumbersome and costly.
Innovation Solution
A system and method utilizing machine learning models, such as neural networks and random forests, to rapidly predict IC design metrics like power, performance, and area, enabling operator-specific feedback and direct mapping to design features, facilitating precise identification of bottlenecks and streamlining optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional IC design tools are used for synthesis, then comprehensive design analysis can be performed, but computation time increases to hours or days
Solution Approach 1:
The patent segments the IC design analysis into operator-level components, where each operator is analyzed independently by specialized statistical models. This segmentation allows parallel processing of multiple operators, reducing overall computation time while maintaining comprehensive analysis coverage through aggregation of individual operator metrics.
Solution Approach 2:
The patent introduces statistical models as intermediary components between the IC design specification and the synthesis tool. These statistical models provide rapid predictions of design metrics, serving as a mediator that delivers comprehensive analysis results without requiring full synthesis execution, thereby reducing computation time.
2Measurement precision
If conventional IC design tools are used, then detailed design analysis is possible, but availability is limited and costly
Solution Approach 1:
The patent creates simplified copies of the synthesis tool's functionality through statistical models trained on synthesis tool outputs. These statistical models replicate the metric prediction capabilities of the synthesis tool but execute much faster and can be deployed more widely, improving availability while maintaining metrics accuracy.
Solution Approach 2:
The patent changes the computational parameters from full synthesis execution to statistical model evaluation. By pre-training statistical models on synthesis tool data and then using them for rapid prediction, the system achieves similar metric accuracy with dramatically improved availability and reduced computational requirements.
3Reliability
If conventional synthesis tools are used, then complete IC modeling is achieved, but the relationship between design specifications and performance metrics becomes obscured
Solution Approach 1:
The patent segments the analysis back to the operator level, maintaining a direct mapping between design specification operators and predicted metrics. This segmentation preserves the relationship between design decisions and outcomes by analyzing each operator independently and aggregating results, preventing the obscuring effect of complete IC modeling.
Solution Approach 2:
The patent implements feedback by providing operator-level metric predictions that directly correlate to specific design specification elements. This feedback mechanism maintains visibility into the relationship between design choices and performance outcomes, enabling designers to understand the impact of specific operators on overall IC performance.
4Manufacturing precision
If iterative design optimization is performed using conventional tools, then design improvements can be achieved, but the process becomes cumbersome and costly
Solution Approach 1:
The patent performs preliminary analysis using statistical models before committing to full synthesis iterations. By obtaining rapid metric predictions early in the design process, designers can identify and resolve issues before expensive iterative synthesis cycles, improving optimization quality while increasing productivity through reduced iteration costs.
Solution Approach 2:
The patent uses statistical model copies to simulate synthesis outcomes rapidly, allowing multiple design iterations to be evaluated at low cost. This copying approach enables extensive iterative optimization without the cumulative cost and time burden of running full synthesis tools for each iteration, thereby improving both optimization quality and design cycle efficiency.
Data Source
AI summary
A method for integrated circuit design, preferably including: determining a model, determining an input, and/or providing predictions. A system for integrated circuit design, preferably including: a training module, an input module, a prediction module, an operator model, a scaling model, and/or one or more computing systems. In some variants, the system and/or method can function to provide rapid predictions of integrated circuit metrics, such as power, performance, area, and/or the like, associated with one or more integrated circuit designs.