Ansys, Inc.

Ying-Shiun Li

Norman H Chang

Deqi Zhu

Jimin Wen

Hsiming Pan

Akhilesh Kumar

Wen-Tze Chuang

Wenbo Xia

Karthik Srinivasan

En-Cih Yang

Joao Moreno Geada

Yaowei Jia

Sankar Ramachandran

Hao Zhuang

Suresh Kumar Mantena

Ganesh Tsavatapalli

Rahul Rajan

Mathew Joseph Kaipanatu

Machine assisted systems and methods for enhancing the resolution of an IC thermal profile from a system analysis are described. These systems and methods can use a neural network based predictor, that has been trained to determine a temperature rise across an entire IC. The training of the predictor can include generating a representation of two or more templates identifying different portions of an integrated circuit (IC), each template associated with location parameters to position the template in the IC; performing thermal simulations for each respective template of the IC, each thermal simulation determining an output based on a power pattern of tiles of the respective template, the output indicating a change in temperature of a center tile of the respective template relative to a base temperature of the integrated circuit; and training a neural network. The trained predictor can be used to determine a temperature rise and then can be appended to a system level thermal profile of the IC to generate a detailed thermal profile of the IC.

Systems and methods for machine learning based fast static thermal solver

Machine assisted systems and methods for enhancing the resolution of an IC thermal profile from a system analysis are described. The methods can include generating a representation of two or more templates identifying different portions of an integrated circuit (IC); performing a thermal simulation for each respective template of the IC based on a sequence of power patterns of tiles of the respective template; and training a neural network with a plurality of training data collected via thermal simulations performed for the templates of the IC. These systems and methods can use a machine learning predictor, that has been trained to determine a transient temperature rise across an entire IC, and then append the determined transient temperature rise to a system level thermal profile of the IC.

Predicting on chip transient thermal response in a multi-chip system using an RNN-based predictor

Example systems and methods are disclosed for performing a timing analysis on a circuit design. A plurality of switching scenarios are identified for the circuit design. One or more predictive models are applied to predict a subset of the plurality of switching scenarios that are likely to cause timing paths with critical timing problems. A dynamic voltage analysis is performed on timing paths based on the subset of switching scenarios. The one or more predictive models are applied to predict a set of critical timing paths based on the subset of switching scenarios that are likely to cause critical timing problems, the one or more predictive models taking into account the dynamic voltage analysis. A timing analysis is the performed on the set of critical timing paths.

Timing assistant for dynamic voltage drop impact on setup/hold constraints

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Pleasanton, CA, United States of America

Ying-Shiun Li