Abstract: When designing circuits to meet certain constraint requirements, it is challenging to determine whether a given circuit design will meet the constraints. A designer at an early stage of the circuit design (e.g., synthesis or placement) may have limited information to rely on in order to determine whether the eventual circuit, or some design variation thereof, will satisfy those constraints without fully designing the circuit. The approaches described herein use a machine learning (ML) model to predict, based on features of partial circuit designs at early stages of the design flow, whether the full circuit is likely to meet the constraints. Additionally, the disclosed approaches allow for the ranking of various circuit designs or design implementations to determine best candidates to proceed with the full design.

Abstract: Training data is collected for each training integrated circuit (IC) design of a set of training IC designs by: extracting a first set of IC design features in a first stage of an IC design flow, and extracting a first set of IC design labels in a second stage of the IC design flow, where the first stage of the IC design flow occurs earlier than the second stage of the IC design flow in the IC design flow. Next, a machine learning model is trained based on the training data.

Abstract: When designing circuits to meet certain constraint requirements, it is challenging to determine whether a given circuit design will meet the constraints. A designer at an early stage of the circuit design (e.g., synthesis or placement) may have limited information to rely on in order to determine whether the eventual circuit, or some design variation thereof, will satisfy those constraints without fully designing the circuit. The approaches described herein use a machine learning (ML) model to predict, based on features of partial circuit designs at early stages of the design flow, whether the full circuit is likely to meet the constraints. Additionally, the disclosed approaches allow for the ranking of various circuit designs or design implementations to determine best candidates to proceed with the full design.

Abstract: Training data is collected for each training integrated circuit (IC) design of a set of training IC designs by: extracting a first set of IC design features in a first stage of an IC design flow, and extracting a first set of IC design labels in a second stage of the IC design flow, where the first stage of the IC design flow occurs earlier than the second stage of the IC design flow in the IC design flow. Next, a machine learning model is trained based on the training data.