Krishnakumar Nair

According to our database, Krishnakumar Nair authored at least 21 papers between 2011 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

PIFS-Rec: Process-In-Fabric-Switch for Large-Scale Recommendation System Inferences.

Pingyi Huo, Anusha Devulapally, Hasan Al Maruf, Minseo Park, Krishnakumar Nair, Meena Arunachalam, Gulsum Gudukbay Akbulut, Mahmut Taylan Kandemir, Vijaykrishnan Narayanan

CoRR, 2024

2023

XRBench: An Extended Reality (XR) Machine Learning Benchmark Suite for the Metaverse.

Hyoukjun Kwon, Krishnakumar Nair, Jamin Seo, Jason Yik, Debabrata Mohapatra, Dongyuan Zhan, Jinook Song, Peter Capak, Peizhao Zhang, Peter Vajda, Colby R. Banbury, Mark Mazumder, Liangzhen Lai, Ashish Sirasao, Tushar Krishna, Harshit Khaitan, Vikas Chandra, Vijay Janapa Reddi

Proceedings of the Sixth Conference on Machine Learning and Systems, 2023

MTIA: First Generation Silicon Targeting Meta's Recommendation Systems.

Amin Firoozshahian, Joel Coburn, Roman Levenstein, Rakesh Nattoji, Ashwin Kamath, Olívia Wu, Gurdeepak Grewal, Harish Aepala, Bhasker Jakka, Bob Dreyer, Adam Hutchin, Utku Diril, Krishnakumar Nair, Ehsan K. Ardestani, Martin Schatz, Yuchen Hao, Rakesh Komuravelli, Kunming Ho, Sameer Abu Asal, Joe Shajrawi, Kevin Quinn, Nagesh Sreedhara, Pankaj Kansal, Willie Wei, Dheepak Jayaraman, Linda Cheng, Pritam Chopda, Eric Wang, Ajay Bikumandla, Arun Karthik Sengottuvel, Krishna Thottempudi, Ashwin Narasimha, Brian Dodds, Cao Gao, Jiyuan Zhang, Mohammed Al-Sanabani, Ana Zehtabioskuie, Jordan Fix, Hangchen Yu, Richard Li, Kaustubh Gondkar, Jack Montgomery, Mike Tsai, Saritha Dwarakapuram, Sanjay Desai, Nili Avidan, Poorvaja Ramani, Karthik Narayanan, Ajit Mathews, Sethu Gopal, Maxim Naumov, Vijay Rao, Krishna Noru, Harikrishna Reddy, Prahlad Venkatapuram, Alexis Bjorlin

Proceedings of the 50th Annual International Symposium on Computer Architecture, 2023

2022

Learning to Collide: Recommendation System Model Compression with Learned Hash Functions.

Benjamin Ghaemmaghami, Mustafa Ozdal, Rakesh Komuravelli, Dmitriy Korchev, Dheevatsa Mudigere, Krishnakumar Nair, Maxim Naumov

CoRR, 2022

An In-Silico model for evaluating the directional shock vectors in terminating and modulating rotors.

Nikhil Valsan Kulangareth, Karl Magtibay, Stéphane Massé, Krishnakumar Nair, Paul Dorian, Kumaraswamy Nanthakumar, Karthikeyan Umapathy

Comput. Biol. Medicine, 2022

Check-N-Run: a Checkpointing System for Training Deep Learning Recommendation Models.

Assaf Eisenman, Kiran Kumar Matam, Steven Ingram, Dheevatsa Mudigere, Raghuraman Krishnamoorthi, Krishnakumar Nair, Misha Smelyanskiy, Murali Annavaram

Proceedings of the 19th USENIX Symposium on Networked Systems Design and Implementation, 2022

Software-hardware co-design for fast and scalable training of deep learning recommendation models.

Dheevatsa Mudigere, Yuchen Hao, Jianyu Huang, Zhihao Jia, Andrew Tulloch, Srinivas Sridharan, Xing Liu, Mustafa Ozdal, Jade Nie, Jongsoo Park, Liang Luo, Jie Amy Yang, Leon Gao, Dmytro Ivchenko, Aarti Basant, Yuxi Hu, Jiyan Yang, Ehsan K. Ardestani, Xiaodong Wang, Rakesh Komuravelli, Ching-Hsiang Chu, Serhat Yilmaz, Huayu Li, Jiyuan Qian, Zhuobo Feng, Yinbin Ma, Junjie Yang, Ellie Wen, Hong Li, Lin Yang, Chonglin Sun, Whitney Zhao, Dimitry Melts, Krishna Dhulipala, K. R. Kishore, Tyler Graf, Assaf Eisenman, Kiran Kumar Matam, Adi Gangidi, Guoqiang Jerry Chen, Manoj Krishnan, Avinash Nayak, Krishnakumar Nair, Bharath Muthiah, Mahmoud khorashadi, Pallab Bhattacharya, Petr Lapukhov, Maxim Naumov, Ajit Mathews, Lin Qiao, Mikhail Smelyanskiy, Bill Jia, Vijay Rao

Proceedings of the ISCA '22: The 49th Annual International Symposium on Computer Architecture, New York, New York, USA, June 18, 2022

Supporting Massive DLRM Inference through Software Defined Memory.

Ehsan K. Ardestani, Changkyu Kim, Seung Jae Lee, Luoshang Pan, Jens Axboe, Valmiki Rampersad, Banit Agrawal, Fuxun Yu, Ansha Yu, Trung Le, Hector Yuen, Dheevatsa Mudigere, Shishir Juluri, Akshat Nanda, Manoj Wodekar, Krishnakumar Nair, Maxim Naumov, Chris Petersen, Mikhail Smelyanskiy, Vijay Rao

Proceedings of the 42nd IEEE International Conference on Distributed Computing Systems, 2022

2021

Supporting Massive DLRM Inference Through Software Defined Memory.

Ehsan K. Ardestani, Changkyu Kim, Seung Jae Lee, Luoshang Pan, Valmiki Rampersad, Jens Axboe, Banit Agrawal, Fuxun Yu, Ansha Yu, Trung Le, Hector Yuen, Shishir Juluri, Akshat Nanda, Manoj Wodekar, Dheevatsa Mudigere, Krishnakumar Nair, Maxim Naumov, Chris Peterson, Mikhail Smelyanskiy, Vijay Rao

CoRR, 2021

High-performance, Distributed Training of Large-scale Deep Learning Recommendation Models.

Dheevatsa Mudigere, Yuchen Hao, Jianyu Huang, Andrew Tulloch, Srinivas Sridharan, Xing Liu, Mustafa Ozdal, Jade Nie, Jongsoo Park, Liang Luo, Jie Amy Yang, Leon Gao, Dmytro Ivchenko, Aarti Basant, Yuxi Hu, Jiyan Yang, Ehsan K. Ardestani, Xiaodong Wang, Rakesh Komuravelli, Ching-Hsiang Chu, Serhat Yilmaz, Huayu Li, Jiyuan Qian, Zhuobo Feng, Yinbin Ma, Junjie Yang, Ellie Wen, Hong Li, Lin Yang, Chonglin Sun, Whitney Zhao, Dimitry Melts, Krishna Dhulipala, K. R. Kishore, Tyler Graf, Assaf Eisenman, Kiran Kumar Matam, Adi Gangidi, Guoqiang Jerry Chen, Manoj Krishnan, Avinash Nayak, Krishnakumar Nair, Bharath Muthiah, Mahmoud khorashadi, Pallab Bhattacharya, Petr Lapukhov, Maxim Naumov, Lin Qiao, Mikhail Smelyanskiy, Bill Jia, Vijay Rao

CoRR, 2021

2020

Check-N-Run: A Checkpointing System for Training Recommendation Models.

Assaf Eisenman, Kiran Kumar Matam, Steven Ingram, Dheevatsa Mudigere, Raghuraman Krishnamoorthi, Murali Annavaram, Krishnakumar Nair, Misha Smelyanskiy

CoRR, 2020

Deep Learning Training in Facebook Data Centers: Design of Scale-up and Scale-out Systems.

Maxim Naumov, John Kim, Dheevatsa Mudigere, Srinivas Sridharan, Xiaodong Wang, Whitney Zhao, Serhat Yilmaz, Changkyu Kim, Hector Yuen, Mustafa Ozdal, Krishnakumar Nair, Isabel Gao, Bor-Yiing Su, Jiyan Yang, Mikhail Smelyanskiy

CoRR, 2020

Scalable Distributed Training of Recommendation Models: An ASTRA-SIM + NS3 case-study with TCP/IP transport.

Saeed Rashidi, Pallavi Shurpali, Srinivas Sridharan, Naader Hassani, Dheevatsa Mudigere, Krishnakumar Nair, Misha Smelyanski, Tushar Krishna

Proceedings of the IEEE Symposium on High-Performance Interconnects, 2020

2019

Empirical mode decomposition based ECG features in classifying and tracking ventricular arrhythmias.

Matthew Hotradat, Krishnanand Balasundaram, Stéphane Massé, Krishnakumar Nair, Kumaraswamy Nanthakumar, Karthikeyan Umapathy

Comput. Biol. Medicine, 2019

2018

Instantaneous Time-Frequency Features in Characterizing Ventricular Arrhythmias Using Empirical Mode Decomposition.

Matthew Hotradat, Krishnanand Balasundaram, Stéphane Massé, Krishnakumar Nair, Kumaraswamy Nanthakumar, Karthikeyan Umapathy

Proceedings of the 52nd Asilomar Conference on Signals, Systems, and Computers, 2018

2017

Morphologically constrained signal subspace characterization of electrograms during ventricular fibrillation.

Krishnanand Balasundaram, Stéphane Massé, Talha Farid, Krishnakumar Nair, John Asta, Robert J. Cusimano, Edward J. Vigmond, Kumaraswamy Nanthakumar, Karthikeyan Umapathy

Biomed. Signal Process. Control., 2017

2013

A classification scheme for ventricular arrhythmias using wavelets analysis.

Krishnanand Balasundaram, Stéphane Massé, Krishnakumar Nair, Karthikeyan Umapathy

Medical Biol. Eng. Comput., 2013

Automated signal pattern detection in ECG during human ventricular arrhythmias.

Krishnanand Balasundaram, Stéphane Massé, Krishnakumar Nair, Karthikeyan Umapathy

Proceedings of the 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2013

2011

Relating spatial heterogeneities to rotor formation in studying human ventricular fibrillation.

J. Jeyaratnam, Karthikeyan Umapathy, Stéphane Massé, Krishnakumar Nair, Talha Farid, Sridhar Krishnan, Kumaraswamy Nanthakumar

Proceedings of the 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2011

Wavelet-based features for characterizing ventricular arrhythmias in optimizing treatment options.

Krishnanand Balasundaram, Stéphane Massé, Krishnakumar Nair, Talha Farid, Kumaraswamy Nanthakumar, Karthikeyan Umapathy

Proceedings of the 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2011

Predicting refibrillation from pre-shock waveforms in optimizing cardiac resuscitation.

E. Afatmirni, Kumaraswamy Nanthakumar, Stéphane Massé, Krishnakumar Nair, Talha Farid, Sridhar Krishnan, Paul Dorian, Karthikeyan Umapathy

Proceedings of the 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2011