ZipNN: Lossless Compression for AI Models

Autor: Hershcovitch, Moshik, Wood, Andrew, Choshen, Leshem, Girmonsky, Guy, Leibovitz, Roy, Ennmouri, Ilias, Malka, Michal, Chin, Peter, Sundararaman, Swaminathan, Harnik, Danny

With the growth of model sizes and the scale of their deployment, their sheer size burdens the infrastructure requiring more network and more storage to accommodate these. While there is a vast model compression literature deleting parts of the model

Externí odkaz: http://arxiv.org/abs/2411.05239

Deformation Recovery: Localized Learning for Detail-Preserving Deformations

Autor: Sundararaman, Ramana, Donati, Nicolas, Melzi, Simone, Corman, Etienne, Ovsjanikov, Maks

We introduce a novel data-driven approach aimed at designing high-quality shape deformations based on a coarse localized input signal. Unlike previous data-driven methods that require a global shape encoding, we observe that detail-preserving deforma

Externí odkaz: http://arxiv.org/abs/2410.08225

Minimizing the Active Power Losses and Retaining the Voltage Profile of the Distribution System Using Soft Computing Techniques with DG Source

Autor: P. Sundararaman, E. Mohan, S. V. Aswin Kumer, Sridhar Udayakumar, Abdissa Fekadu Moti

Publikováno v: Journal of Electrical and Computer Engineering, Vol 2022 (2022)

The motivation of this research is to curtail the active power losses in the radial distribution system. This paper proposes a novel voltage stability indicator to determine the optimum conductor sizes for various distribution lines and find the cond

Externí odkaz: https://doaj.org/article/a6968838c89543fba5cd115d9a40026a

The infrastructure powering IBM's Gen AI model development

Autor: Gershon, Talia, Seelam, Seetharami, Belgodere, Brian, Bonilla, Milton, Hoang, Lan, Barnett, Danny, Chung, I-Hsin, Mohan, Apoorve, Chen, Ming-Hung, Luo, Lixiang, Walkup, Robert, Evangelinos, Constantinos, Salaria, Shweta, Dombrowa, Marc, Park, Yoonho, Kayi, Apo, Schour, Liran, Alim, Alim, Sydney, Ali, Maniotis, Pavlos, Schares, Laurent, Metzler, Bernard, Karacali-Akyamac, Bengi, Wen, Sophia, Chiba, Tatsuhiro, Choochotkaew, Sunyanan, Yoshimura, Takeshi, Misale, Claudia, Elengikal, Tonia, Connor, Kevin O, Liu, Zhuoran, Molina, Richard, Schneidenbach, Lars, Caden, James, Laibinis, Christopher, Fonseca, Carlos, Tarasov, Vasily, Sundararaman, Swaminathan, Schmuck, Frank, Guthridge, Scott, Cohn, Jeremy, Eshel, Marc, Muench, Paul, Liu, Runyu, Pointer, William, Wyskida, Drew, Krull, Bob, Rose, Ray, Wolfe, Brent, Cornejo, William, Walter, John, Malone, Colm, Perucci, Clifford, Franco, Frank, Hinds, Nigel, Calio, Bob, Druyan, Pavel, Kilduff, Robert, Kienle, John, McStay, Connor, Figueroa, Andrew, Connolly, Matthew, Fost, Edie, Roma, Gina, Fonseca, Jake, Levy, Ido, Payne, Michele, Schenkel, Ryan, Malki, Amir, Schneider, Lion, Narkhede, Aniruddha, Moshref, Shekeba, Kisin, Alexandra, Dodin, Olga, Rippon, Bill, Wrieth, Henry, Ganci, John, Colino, Johnny, Habeger-Rose, Donna, Pandey, Rakesh, Gidh, Aditya, Gaur, Aditya, Patterson, Dennis, Salmani, Samsuddin, Varma, Rambilas, Rumana, Rumana, Sharma, Shubham, Mishra, Mayank, Panda, Rameswar, Prasad, Aditya, Stallone, Matt, Zhang, Gaoyuan, Shen, Yikang, Cox, David, Puri, Ruchir, Agrawal, Dakshi, Thorstensen, Drew, Belog, Joel, Tang, Brent, Gupta, Saurabh Kumar, Biswas, Amitabha, Maheshwari, Anup, Gampel, Eran, Van Patten, Jason, Runion, Matthew, Kaki, Sai, Bogin, Yigal, Reitz, Brian, Pritko, Steve, Najam, Shahan, Nambala, Surya, Chirra, Radhika, Welp, Rick, DiMitri, Frank, Telles, Felipe, Arvelo, Amilcar, Chu, King, Seminaro, Ed, Schram, Andrew, Eickhoff, Felix, Hanson, William, Mckeever, Eric, Joseph, Dinakaran, Chaudhary, Piyush, Shivam, Piyush, Chaudhary, Puneet, Jones, Wesley, Guthrie, Robert, Bostic, Chris, Islam, Rezaul, Duersch, Steve, Sawdon, Wayne, Lewars, John, Klos, Matthew, Spriggs, Michael, McMillan, Bill, Gao, George, Kamra, Ashish, Singh, Gaurav, Curry, Marc, Katarki, Tushar, Talerico, Joe, Shi, Zenghui, Malleni, Sai Sindhur, Gallen, Erwan

AI Infrastructure plays a key role in the speed and cost-competitiveness of developing and deploying advanced AI models. The current demand for powerful AI infrastructure for model training is driven by the emergence of generative AI and foundational

Externí odkaz: http://arxiv.org/abs/2407.05467

Static Subspace Approximation for Random Phase Approximation Correlation Energies: Implementation and Performance

Autor: Weinberg, Daniel, Hull, Olivia A., Clary, Jacob M., Sundararaman, Ravishankar, Vigil-Fowler, Derek, Del Ben, Mauro

Developing theoretical understanding of complex reactions and processes at interfaces requires using methods that go beyond semilocal density functional theory to accurately describe the interactions between solvent, reactants and substrates. Methods

Externí odkaz: http://arxiv.org/abs/2405.20258