Machine Learning Based Design Space Exploration and Applications to Signal Integrity Analysis of 112Gb SerDes Systems

Autor:	Alex Manukovsky, Zurab Khasidashvili, Yuriy Shlepnev
Rok vydání:	2021
Předmět:	business.industry Computer science Design space exploration Operating margin SerDes Machine learning computer.software_genre Space exploration Systems analysis visual_art Electronic component visual_art.visual_art_medium Systems design Artificial intelligence business computer Communication channel
Zdroj:	2021 IEEE 71st Electronic Components and Technology Conference (ECTC).
DOI:	10.1109/ectc32696.2021.00201
Popis:	This paper describes a systematic approach for system design space exploration through the application of machine learning (ML) methods for advanced system analysis. A demonstration of applying this method for signal integrity analysis, and a case study of 112Gb SerDes systems analysis based on Channel Operating Margin (COM) simulation methodology, are provided.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::ffb692810c89ab6de0aa60ca9f7d8c5d https://doi.org/10.1109/ectc32696.2021.00201 Zobrazit plný text záznamu