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Machine Learning Based Method Of Moments (Ml-Mom), He Ming Yao, Li (Lijun) Jun Jiang, Yu Wei Qin
Machine Learning Based Method Of Moments (Ml-Mom), He Ming Yao, Li (Lijun) Jun Jiang, Yu Wei Qin
Electrical and Computer Engineering Faculty Research & Creative Works
This paper proposes a novel method by rethinking the method of moments (MoM) solving process into a machine learning training process. Based on the artificial neural network (ANN), the conventional MoM matrix is treated as the training data set, based on which machine learning training process becomes conventional linear algebra MoM solving process. The trained result is the solution of MoM. The multiple linear regression (MLR) is utilized to train the model. Amazon Web Service (AWS) is used as the computations platform to utilize the existing hardware and software resources for machine learning. To verify the feasibility of the proposed …
Machine Learning Based Mom (Ml-Mom) For Parasitic Capacitance Extractions, He Ming Yao, Yu Wei Qin, Li (Lijun) Jun Jiang
Machine Learning Based Mom (Ml-Mom) For Parasitic Capacitance Extractions, He Ming Yao, Yu Wei Qin, Li (Lijun) Jun Jiang
Electrical and Computer Engineering Faculty Research & Creative Works
This paper is a rethinking of the conventional method of moments (MoM) using the modern machine learning (ML) technology. By repositioning the MoM matrix and unknowns in an artificial neural network (ANN), the conventional linear algebra MoM solving is changed into a machine learning training process. The trained result is the solution. As an application, the parasitic capacitance extraction broadly needed by VLSI modeling is solved through the proposed new machine learning based method of moments (ML-MoM). The multiple linear regression (MLR) is employed to train the model. The computations are done on Amazon Web Service (AWS). Benchmarks demonstrated the …