Ambasana, Nikita and Gope, Dipanjan and Mutnury, Bhyrav and Anand, Gowri (2015) Automated Frequency Selection for Machine-Learning based EH/EW prediction from S-Parameters. In: 24th IEEE Conference on Electrical Performance of Electronic Packaging and Systems (EPEPS), OCT 25-28, 2015, San Jose, CA, pp. 53-55.
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Abstract
In the field of High Speed SerDes (HSS) channel analysis and design, the most widely accepted metrics for gauging signal integrity are Time Domain (TD) metrics: Bit Error Rate (BER), Eye-Height (EH) and Eye-Width (EW). With increasing bit-rates, TD simulations are getting compute-time intensive especially as the BER criterion is getting lower. Learning based mapping of Frequency Domain (FD) S-Parameter data to EH/EW in TD provides a fast alternative solution for thorough design-space exploration. A key challenge in this mapping procedure is the identification of the optimal frequency points in the S-Parameter data that are used for training the learning network. This paper outlines a methodology to identify the minimal set of critical frequency points using a Fast Correlation Based Feature (FCBF) selection algorithm. This technique is applied for prediction of EH/EW for a PCIe Gen 3 interface and the prediction accuracy is quantified.
Item Type: | Conference Proceedings |
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Series.: | IEEE Conference on Electrical Performance of Electronic Packaging and Systems-EPEPS |
Publisher: | IEEE |
Additional Information: | Copy Right of this article belongs to the IEEE, 345 E 47TH ST, NEW YORK, NY 10017 USA |
Keywords: | Signal Integrity; PCIe; ANN; Eye-Height; Total FEXT; Insertion Loss; Feature Selection |
Department/Centre: | Division of Electrical Sciences > Electrical Communication Engineering |
Date Deposited: | 20 Jul 2016 09:09 |
Last Modified: | 20 Jul 2016 09:09 |
URI: | http://eprints.iisc.ac.in/id/eprint/54229 |
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