ML-Based Component Placement for Analog Circuit Design
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Solution Overview
Problem
The process of placing analog circuits on integrated circuits is labor-intensive and time-consuming due to increasing design constraints and intricate physical effects, with existing electronic design automation tools relying heavily on manual drawing, which is error-prone and inefficient.
Innovation Solution
An electronic design platform utilizing a metaheuristic algorithm and model-based reinforcement learning (RL) to logically place components on an electronic design real estate, iteratively enhancing the architectural design placement through re-evaluation and re-training of probabilistic functions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual drawing is used to place components in electronic design tools, then designers can control component placement, but the process becomes labor-intensive and time-consuming
Solution Approach 1:
The system enables automated component placement where the EDA tool performs the placement task autonomously using machine learning models, eliminating the need for manual drawing while maintaining optimized placement results. The ML model learns from training data and automatically determines component positions based on design constraints and physical effects.
Solution Approach 2:
The patent replaces the manual mechanical drawing process with an automated computational system. Machine learning algorithms and probabilistic functions substitute for human designers' manual operations, using computational models to predict optimal component placements based on learned patterns from training data.
2Ease of operation
If manual drawing is used for component placement, then designers can adjust placements, but error rates increase and time consumption worsens
Solution Approach 1:
The system incorporates feedback mechanisms where the ML model continuously learns from placement outcomes and design constraints. The probabilistic functions are trained on historical data and iteratively improved based on performance metrics, ensuring high accuracy while maintaining the ability to adjust placements based on feedback from the design process.
3Manufacturing precision
If design constraints increase to meet complex requirements, then design quality improves, but placement complexity and time consumption increase
Solution Approach 1:
The system handles complex design constraints by dynamically adjusting parameters within the ML model. The probabilistic functions incorporate multiple design constraints as input parameters and optimize component placement by varying placement parameters based on the learned relationships between constraints and optimal configurations.
Solution Approach 2:
The ML-based placement system serves multiple functions simultaneously: it considers various design constraints, accounts for physical effects, optimizes component placement, and can be trained on different types of circuits. This multi-functional approach handles complex requirements without proportionally increasing process complexity.
4Quantity of substance
If components are made smaller to increase integration density, then device capacity improves, but placement difficulty and time consumption increase
Solution Approach 1:
The system performs preliminary actions by pre-training ML models on extensive datasets containing placement patterns for various circuit configurations. This pre-training enables the model to quickly make accurate placement decisions for small components without requiring time-consuming manual adjustment, thus maintaining high productivity even as component sizes decrease.
Data Source
AI summary
Electronic design automation (EDA) of the present disclosure logically places components of the electronic circuitry onto an electronic design real estate to determine an architectural design placement for the electronic circuitry. The EDA evaluates a metaheuristic algorithm starting with an initial placement of components of the electronic circuitry onto the electronic design real estate to provide multiple possible placements for placing these components of the electronic circuitry onto the electronic design real estate. The EDA utilizes the multiple possible placements of the metaheuristic algorithm to train one or more probabilistic functions of a model-based reinforcement learning (RL) algorithm. The EDA evaluates the model-based RL algorithm utilizing the one or more probabilistic functions to determine the architectural design placement. The EDA can further iteratively enhance the architectural design placement by re-evaluating the metaheuristic algorithm starting from the architectural design placement as the initial placement of components, re-training the one or more probabilistic functions, and re-evaluating the model-based RL algorithm utilizing the one or more probabilistic functions.