A Data Compression Strategy for the Efficient Uncertainty Quantification of Time-Domain Circuit Responses

IEEE Access

Riccardo Trinchero¹  Paolo Manfredi¹ 

[1] Department of Electronics and Telecommunications, Politecnico di Torino, Torino, Italy;

关键词: High-speed link;    least-squares support-vector machine;    machine learning;    polynomial chaos expansion;    principal component analysis;    signal integrity;   

DOI  :  10.1109/ACCESS.2020.2993338

来源: DOAJ

【 摘 要 】

This paper presents an innovative modeling strategy for the construction of efficient and compact surrogate models for the uncertainty quantification of time-domain responses of digital links. The proposed approach relies on a two-step methodology. First, the initial dataset of available training responses is compressed via principal component analysis (PCA). Then, the compressed dataset is used to train compact surrogate models for the reduced PCA variables using advanced techniques for uncertainty quantification and parametric macromodeling. Specifically, in this work sparse polynomial chaos expansion and least-square support-vector machine regression are used, although the proposed methodology is general and applicable to any surrogate modeling strategy. The preliminary compression allows limiting the number and complexity of the surrogate models, thus leading to a substantial improvement in the efficiency. The feasibility and performance of the proposed approach are investigated by means of two digital link designs with 54 and 115 uncertain parameters, respectively.

【 授权许可】

Unknown