IC Metric Prediction Models for Fast Design Space Exploration
Find Innovative SolutionsGenerate Solutions
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, providing operator-specific feedback to facilitate direct mapping and traceability, enabling precise identification of design bottlenecks and accelerating the optimization process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional IC design tools are used for synthesis, then comprehensive design analysis is achieved, but computation time increases to hours or days
Solution Approach 1:
The patent pre-trains machine learning models using comprehensive synthesis tool data before actual design iterations. This preliminary action captures complex design metric relationships in advance, enabling rapid predictions during iterative design without repeating full synthesis computations, thus resolving the time-accuracy contradiction
Solution Approach 2:
The patent creates simplified surrogate models (copies) that approximate the behavior of comprehensive synthesis tools. These ML-based copies provide near-equivalent design metric predictions at a fraction of the computational cost, allowing rapid iteration while maintaining measurement precision
2Reliability
If conventional synthesis tools are used, then complete IC design analysis is performed, but availability is limited and costly
Solution Approach 1:
The patent enables the design system to serve itself by using the ML models for rapid design space exploration and optimization. The comprehensive analysis capability is embedded in the pre-trained models, allowing designers to perform complete design analysis without requiring access to expensive synthesis tools during iteration cycles
Solution Approach 2:
The patent introduces machine learning models as intermediaries between design specifications and synthesis tool outputs. These models act as mediators that provide comprehensive design analysis functionality without requiring direct invocation of limited-availability synthesis tools, thus improving ease of manufacture while maintaining reliability
3Manufacturing precision
If iterative design optimization is performed using conventional tools, then design improvement is achieved, but cognitive burden and cost increase
Solution Approach 1:
The patent implements rapid feedback loops using ML-based predictions that provide immediate design metric evaluations. This enables designers to iteratively optimize design specifications with quick turn-around times, maintaining high manufacturing precision while reducing the cognitive burden associated with waiting for lengthy synthesis results
Solution Approach 2:
The patent applies partial synthesis actions selectively only when needed rather than performing complete synthesis for every design iteration. The ML models handle the majority of optimization iterations, reducing overall process complexity while maintaining design optimization quality through targeted use of comprehensive analysis
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.