System and method for training a neural learning model for predicting performance, power and area behavior of ip components in integrated circuit design

A method for training a neural learning model that optimizes generation of characteristics for any intellectual property (IP) block, the method comprising:

• converting non-numerical data parameters to digital form to produce a machine learning dataset;
• splitting the machine learning dataset into training data and testing data;
• training the neural learning model using the training data;
• testing the trained neural learning model using the testing data to determine accuracy of the neural learning model;
• providing feedback to the neural learning model based on testing;
• deploying the neural learning model to predict one or more characteristics for the IP blocks;
• generating a characterization for each gate level netlist to produce a plurality of characterizations;
• and
• updating the training data using feedback from deploying and predicting.