EDA Technologies for AI adoption

AI has scope of application in different components in EDA and semiconductor flow. 

Simulation is one of the main components in functional verification of chip design. While dealing with complex design, the challenge is in terms of design coverage with generation of test bench input. 

Initially, the test inputs covers a large part of the unexercised design. But, later on, targeted stimuli is required to test specific blocks and corners of design. 

Based on training data with machine learning methods, AI can provide highest ROI for coverage of design. 

AI can accelerate design process as well. In incremental designing and analysis, AI can fast recenter the design and also can improve design process with different technology nodes.

Machine learning algorithms can assess and forecast how various design decisions will affect power and performance, which can result in more energy-efficient solutions. 

AI-adoption in EDA timing tools can enable predictive analysis, like critical timing closure tasks. It can forecast potential timing issues and propose preemptive solutions, reducing time and effort in design flow.

AI can best analyse different design trade-offs by simultaneously considering many design aspects and settings. 

These are difficult for human designers to manually examine.