Srinidhi Ganeshan

ORCID: 0000-0003-3337-9478
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About
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Research Areas
  • Electromagnetic Compatibility and Noise Suppression
  • Lightning and Electromagnetic Phenomena
  • Electrostatic Discharge in Electronics
  • Image and Signal Denoising Methods
  • High voltage insulation and dielectric phenomena
  • Electromagnetic Simulation and Numerical Methods
  • Electromagnetic Scattering and Analysis

Carleton University
2019-2020

Modeling of multiport data characterizing high-speed modules, such as packages, vias, and complex multiconductor interconnects is becoming increasingly important in signal power integrity applications. Vector fitting (VF) algorithm has been widely used by designers for macromodeling system identification from tabulated data. Since VF strategies based on it require many iterations to arrive at an optimal number converged poles, highly desired reduce the computational cost each iteration. This...

10.1109/tcpmt.2020.3004569 article EN IEEE Transactions on Components Packaging and Manufacturing Technology 2020-06-24

System identification from tabulated data has gained significant attention in the recent years for signal and power integrity analysis of high-speed circuits interconnects. Vector Fitting (VF) algorithm been widely used this purpose. Recently GPU based (GVF) was proposed to exploit massive parallel processing capabilities emerging platforms. In paper, a detailed comparative performance study MAGMA cuBLAS libraries while implementing GVF is presented. Results demonstrate that use provide...

10.1109/lascas45839.2020.9069050 article EN 2020-02-01

Vector Fitting (VF) algorithm has been widely used for system identification from multiport tabulated data. Particularly, it is of high interest to the design community focused on modeling high-speed modules such as large number coupled interconnects, packaging structures and variety electromagnetic modules. This paper advances applicability VF exploit emerging massively parallel graphical processing Units (GPUs). Necessary parallelization strategies suitable GPU platforms are developed. For...

10.1109/epeps47316.2019.193201 article EN 2019-10-01
Majid Dolatsara Madhavan Determining Srinidhi Ganeshan Naveen Elumalai Xinying Wang and 95 more Thong Nguyen José E. Schutt‐Ainé Behavioral Modeling José E. Rayas‐Sánchez Francisco E. Rangel-Patiño Benjamín Mercado-Casillas Felipe de J. Leal-Romo José L. Chávez‐Hurtado Daniel Schrögendorfer Thomas Leitner Harald Pretl Del Arnaldo Luiz Robson H. Moreno Carvalho De Paulo H. Ferreira Tales Crepaldi Pimenta Tales Cleber Gordon Mohammed David Rivadeneira Marco Villegas Luis-Miguel Prócel Lionel Optimization Doubler Leonardo Agis Denisse Hardy Kenji Nakasone Alfredo Arnaud Joel Gak Matías Miguez Ronny García-Ramírez Vinicius Borges Murilo Perleberg Vladimir Afonso Marcelo Porto Luciano Low Ryota Ishikawa Masashi Tawada Masao Yanagisawa Nozomu Togawa Eduardo Zummach Roberta Palau Jones Göebel Luciano Agostini Marcelo High Throughput Cdef Carlos Sanabria Mónico Linares Aranda Rogelio Higuera Francisco De La Hidalga David Pollreisz Nima Reliable Thomas Fontanari Guilherme Paim Leandro M. G. Rocha Patrícia Ücker Eduardo Costa Tiago Rohde J.L. Baptista Santos Martins William Medeiros Hamilton Klimach Sergio Lesley Ferreira Mateus Moreira Bárbara Verônica Cardoso de Souza Sandro Binsfeld Ferreira Filipe D. Baumgratz Luis Diego Murillo-Soto Carlos Meza Benavides Faults Roman Fragasse Ramy Tantawy Shane Smith Teressa Specht Zahra Taghipour Phillip Van Hooser Chris Taylor Theodore J. Ronningen Earl Fuller Rudy Fink Sanjay Krishna Waleed Khalil Kota Mizushima Satomi Ogawa Takahide Sato Andry Contreras Leonardo Steinfeld Mariana Siniscalchi Javier Schandy Benigno Blanco Rodríguez Alexandre De Jesus Aragão Dionísio Carvalho Bruno Sanches

Review on the Evolution of Low-

10.1109/lascas45839.2020.9068961 article EN 2020-02-01
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