Fast Analysis of Static IR Drop Effect Based on Machine Learning Methods
Roman Aleksandrovich SOLOVYEV,
Dmitry Vladimirovich TELPUKHOV,
Evgeny Denisovich DEMIDOV,
Ilya Ilyich SHAFEEV https://doi.org/10.15514/ISPRAS-2022-35(5)-9
Abstract
As part of the ICCAD Contest 2023 (Problem C) competition, the paper describes a methodology for applying ML models to perform static IR drop analysis. Methods for obtaining a database for training a neural network to solve this problem are given. We consider a technique for training an ML model to analyze the static IR-drop effect. The generation of input data for training a neural network from SPICE netlists is also discussed in this paper. This solution is ranked in the TOP 3 at the ICCAD Contest 2023 competition.
About the Authors
Roman Aleksandrovich SOLOVYEV
Institute of Design Problems in Microelectronics, RAS (IPPM RAS)
Russian Federation
Russian Federation
Dr. Sci. (Tech.), member-corr. RAS, chief researcher at the Institute of Design Problems in Microelectronics RAS. Area of scientific interests: automation of digital VLSI design, neural networks and machine learning, residue number system, physical synthesis of integrated circuits.
Dmitry Vladimirovich TELPUKHOV
Institute of Design Problems in Microelectronics, RAS (IPPM RAS)
Russian Federation
Russian Federation
Dr. Sci. (Tech.), Deputy Director for Scientific Work of the Institute of Design Problems in Microelectronics RAS. Research interests: automation of digital VLSI design, logic synthesis, radiation-resistant design, machine learning and neural networks, residual class system.
Evgeny Denisovich DEMIDOV
National Research University of Electronic Technology (MIET)
Russian Federation
Russian Federation
2nd year postgraduate student of National Research University "MIET", development engineer computer-aided design tools of VLSI development department at Alphachip Company. Theme of scientific research work: «Development of tools for analyzing IR drop effect using machine learning methods». Research interests: automation of digital VLSI design, special types of VLSI analysis, machine learning and neural networks.
Ilya Ilyich SHAFEEV
Institute of Design Problems in Microelectronics, RAS (IPPM RAS)
Russian Federation
Russian Federation
2nd year master’s student of National Research University "MIET", research-engineer of the Institute of Design Problems in Microelectronics RAS. Research interests: automation of digital VLSI design, special types of VLSI analysis, machine learning and neural networks.
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Review
For citations:
SOLOVYEV R.A., TELPUKHOV D.V., DEMIDOV E.D., SHAFEEV I.I. Fast Analysis of Static IR Drop Effect Based on Machine Learning Methods. Proceedings of the Institute for System Programming of the RAS (Proceedings of ISP RAS). 2023;35(5):127-144. (In Russ.) https://doi.org/10.15514/ISPRAS-2022-35(5)-9
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