Predicting optimal parameters for physical design synthesis

A system, comprising:

• a processor;
• and
• a memory, wherein the memory includes a computer program product configured to perform operations for predicting optimal design flow parameters for physical design synthesis of a given integrated circuit (IC) design, the operations comprising:
• receiving initial design characteristics, design flow parameters, and design targets from a design construction flow for the given integrated circuit (IC) design;
• receiving a historical dataset comprising synthesis design construction flows from historical IC designs;
• training a Variational Autoencoder (VAE) along with a regression network using the historical dataset to provide a training data representation of the dataset constrained to a latent space of the VAE;
• generating feature vectors based on the training data representation of the dataset and updating the feature vectors with the initial design characteristics;
• iteratively performing an input gradient search of the updated feature vectors to optimize an objective function of the design targets to identify locally optimal design parameters, wherein the training data samples are constrained to the initial design characteristics;
• and
• obtaining a prediction of global optimal design flow parameters based on the identified locally optimal design parameters for optimized design targets.

A computer program product for implementing prediction of optimal design flow parameters for physical design synthesis of a given integrated circuit (IC) design, the computer program product comprising:

• a computer-readable storage medium having computer-readable program code embodied therewith, the computer-readable program code executable by one or more computer processors to perform an operation comprising:
• receiving initial design characteristics, design flow parameters, and design targets from a design construction flow for a given integrated circuit (IC) design;
• receiving a historical dataset comprising synthesis design construction flows from historical IC designs;
• training a Variational Autoencoder (VAE) along with a regression network using the historical dataset to provide a training data representation of the dataset constrained to a latent space of the VAE;
• generating feature vectors based on the training data representation of the dataset and updating the feature vectors with the initial design characteristics;
• iteratively performing an input gradient search of the updated feature vectors to optimize an objective function of the design targets to identify locally optimal design parameters, wherein the training data samples are constrained to the initial design characteristics;
• and
• obtaining a prediction of global optimal design flow parameters based on the identified locally optimal design parameters for optimized design targets.