This paper presents an alternative approach for the design of high-speed link based on a preliminary version of a surrogate model for the inverse problem. Specifically, given the overall structure of the link, our goal is to build an accurate and fast-to-evaluate model for the estimation of the geometrical parameters of its interconnect starting from the desired eye diagram characteristics. The modeling scheme proposed in this paper relies on a powerful machine learning regression technique such as the least-squares support vector machine (LS-SVM) which is used to provide an accurate relationship among the desired eye features and the geometrical parameters of the link interconnect. The proposed model is built from a set of training samples generated by a parametric simulation of the link through the full-computational model. The feasibility and the accuracy of the proposed modeling scheme are then investigated by comparing its predictions with the corresponding results provided by the full-computational model on 250 unseen samples.

Design of high-speed links via a machine learning surrogate model for the inverse problem / Trinchero, R.; Dolatsara, M. A.; Roy, K.; Swaminathan, M.; Canavero, F.. - (2019), pp. 1-3. ((Intervento presentato al convegno 2019 Electrical Design of Advanced Packaging and Systems Symposium, EDAPS 2019 tenutosi a Taiwan nel 2019 [10.1109/EDAPS47854.2019.9011627].

Design of high-speed links via a machine learning surrogate model for the inverse problem

Trinchero R.;Canavero F.
2019

Abstract

This paper presents an alternative approach for the design of high-speed link based on a preliminary version of a surrogate model for the inverse problem. Specifically, given the overall structure of the link, our goal is to build an accurate and fast-to-evaluate model for the estimation of the geometrical parameters of its interconnect starting from the desired eye diagram characteristics. The modeling scheme proposed in this paper relies on a powerful machine learning regression technique such as the least-squares support vector machine (LS-SVM) which is used to provide an accurate relationship among the desired eye features and the geometrical parameters of the link interconnect. The proposed model is built from a set of training samples generated by a parametric simulation of the link through the full-computational model. The feasibility and the accuracy of the proposed modeling scheme are then investigated by comparing its predictions with the corresponding results provided by the full-computational model on 250 unseen samples.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11583/2836571