Machine Learning EDA Platform for Chip Design Optimization
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Solution Overview
Problem
Conventional electronic design automation (EDA) tools require a time-consuming trial and error process for optimizing the placement and routing of standard library cells, leading to increased time to market and unnecessary duplication of design, simulation, and verification iterations.
Innovation Solution
The implementation of a machine learning-based electronic design automation platform that optimizes electronic architectural designs by adjusting geometric shapes, locations, and interconnections over multiple iterations to satisfy electronic design targets, reducing the need for manual adjustments and streamlining the design, simulation, and verification processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If conventional software tools are used for electronic device design, then design completeness is achieved, but time to market increases due to manual trial and error optimization
Solution Approach 1:
The system performs self-service by automatically optimizing placement and routing of standard library cells through machine learning algorithms without requiring manual designer intervention. The EDA tool independently iterates through optimization cycles, adjusting cell positions and connections to meet design specifications, thereby eliminating the time-consuming manual trial-and-error process while maintaining design completeness.
2Productivity
If manual trial and error optimization is performed, then design requirements are satisfied, but productivity decreases due to repetitive iterations
Solution Approach 1:
The patent replaces the mechanical manual trial-and-error process with an automated computational system using machine learning algorithms. The system substitutes human designer actions with algorithmic optimization that automatically adjusts placement and routing parameters, maintaining precision while dramatically improving productivity by eliminating repetitive manual iterations.
3Loss of time
If conventional design tools complete placement and routing before optimization, then design structure is established, but time is wasted duplicating design, simulation, and verification across iterations
Solution Approach 1:
The system performs preliminary action by establishing the complete design structure including placement and routing before initiating optimization iterations. This preliminary establishment allows subsequent optimization cycles to focus solely on improving existing structures rather than recreating them, thereby eliminating duplicate design, simulation, and verification work while managing process complexity through structured automation.
Data Source
AI summary
Electronic design automation (EDA) of the present disclosure, in various embodiments, optimizes designing, simulating, analyzing, and verifying of one or more electronic architectural designs for an electronic device. The EDA of the present disclosure identifies one or more electronic architectural features from the one or more electronic architectural designs. In some situations, the EDA of the present disclosure can manipulate one or more electronic architectural models over multiple iterations using a machine learning process until one or more electronic architectural models from among the one or more electronic architectural models satisfy one or more electronic design targets. The EDA of the present disclosure substitutes the one or more electronic architectural models that satisfy the one or more electronic design targets for the one or more electronic architectural features in the one or more electronic architectural designs to optimize the one or more electronic architectural designs. The EDA of the present disclosure can substitute the one or more electronic architectural models before, during, and/or after designing, simulating, analyzing, and/or verifying of the one or more electronic architectural designs to effectively decrease the time to market (TTM) for the electronic device.