ML Constraint Optimization for Semiconductor Circuit Speed
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Solution Overview
Problem
Current Electronic Design Automation (EDA) tools lack the capability to efficiently suggest optimized constraint configurations for integrated circuit design, leading to prolonged simulation times and lower performance results due to the need for manual input and extensive simulation rounds.
Innovation Solution
A system utilizing a processor and non-transitory computer-readable storage medium that includes a constraint library, circuit speed result library, and instructions to execute a benchmark platform, circuit speed result collector, and agent, which performs emulation processes to suggest optimized constraint configurations using reinforcement-learning or machine-learning agents to select promising constraint configurations for improved circuit speed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual input and extensive simulation rounds are used to find optimized constraint configurations, then thorough verification is achieved, but simulation time is prolonged and design efficiency is reduced
Solution Approach 1:
The EDA tool automatically performs constraint configuration optimization through machine learning models without requiring manual intervention. The system self-services by generating, evaluating, and selecting optimized constraint configurations autonomously, eliminating the need for manual input while maintaining thorough verification through multiple simulation rounds.
Solution Approach 2:
The system performs preliminary constraint configuration generation and evaluation before full-scale simulation. Machine learning models predict promising constraint configurations in advance, allowing the system to pre-filter candidate configurations and focus extensive simulation rounds only on the most promising options, thereby reducing overall simulation time while maintaining reliability.
2Manufacturing precision
If multiple constraint configurations are evaluated through extensive simulation, then optimal performance is achieved, but design productivity is reduced
Solution Approach 1:
The system performs partial simulation evaluation on a large number of constraint configurations by using machine learning models to predict performance metrics. Instead of running full simulations on all possible configurations, the system applies partial action by evaluating only the most promising candidates in detail, achieving optimal performance identification while maintaining high design productivity.
Solution Approach 2:
The patent replaces the traditional mechanical simulation-based constraint optimization approach with machine learning-based prediction systems. This substitution allows for rapid evaluation of multiple constraint configurations without the computational overhead of extensive circuit simulations, thereby achieving optimal performance identification while significantly improving design efficiency.
3Productivity
If automated machine-learning agents are used to suggest constraint configurations, then design efficiency is improved, but system complexity increases
Solution Approach 1:
The EDA tool integrates multiple functions into a unified automated constraint optimization system. The machine learning agent simultaneously performs constraint configuration generation, performance prediction, and optimization selection, eliminating the need for separate manual processes. This multi-functionality improves design efficiency while managing system complexity through integration rather than addition of separate components.
Data Source
AI summary
A method, for determining constraints related to a target circuit, includes following operations. First circuit speed results of the target circuit under different candidate constraint configurations are accumulated. Breakthrough probability distributions relative to each of the candidate constraint configurations are determined according to the first circuit speed results. First selected constraint configurations are determined from the candidate constraint configurations by sampling the breakthrough probability distributions. A first budget distribution is determined among the first selected constraint configurations. In response to that the first budget distribution is converged, the first selected constraint configurations in the first budget distribution is utilized for implementing the target circuit and generating an updated circuit speed result of the target circuit.