Predicting refibrillation from pre-shock waveforms in optimizing cardiac resuscitation. - E. Afatmirni; Kumaraswamy Nanthakumar; Stéphane Massé; Krishnakumar Nair; Talha Farid; Sridhar Krishnan 001; Paul Dorian; Karthikeyan Umapathy (EMBC, 2011)

Wavelet-based features for characterizing ventricular arrhythmias in optimizing treatment options. - Krishnanand Balasundaram; Stéphane Massé; Krishnakumar Nair; Talha Farid; Kumaraswamy Nanthakumar; Karthikeyan Umapathy (EMBC, 2011)

Relating spatial heterogeneities to rotor formation in studying human ventricular fibrillation. - J. Jeyaratnam; Karthikeyan Umapathy; Stéphane Massé; Krishnakumar Nair; Talha Farid; Sridhar Krishnan 001; Kumaraswamy Nanthakumar (EMBC, 2011)

Automated signal pattern detection in ECG during human ventricular arrhythmias. - Krishnanand Balasundaram; Stéphane Massé; Krishnakumar Nair; Karthikeyan Umapathy (EMBC, 2013)

A classification scheme for ventricular arrhythmias using wavelets analysis. - Krishnanand Balasundaram; Stéphane Massé; Krishnakumar Nair; Karthikeyan Umapathy (Medical Biol. Eng. Comput., 2013)

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)

Instantaneous Time-Frequency Features in Characterizing Ventricular Arrhythmias Using Empirical Mode Decomposition. - Matthew Hotradat; Krishnanand Balasundaram; Stéphane Massé; Krishnakumar Nair; Kumaraswamy Nanthakumar; Karthikeyan Umapathy (ACSSC, 2018)

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)

Scalable Distributed Training of Recommendation Models: An ASTRA-SIM + NS3 case-study with TCP/IP transport. - Saeed Rashidi; Pallavi Shurpali; Srinivas Sridharan 002; Naader Hassani; Dheevatsa Mudigere; Krishnakumar Nair; Misha Smelyanskiy; Tushar Krishna (Hot Interconnects, 2020)

Deep Learning Training in Facebook Data Centers: Design of Scale-up and Scale-out Systems. - Maxim Naumov; John Kim 001; Dheevatsa Mudigere; Srinivas Sridharan 002; Xiaodong Wang 020; Whitney Zhao; Serhat Yilmaz; Changkyu Kim; Hector Yuen; Mustafa Ozdal; Krishnakumar Nair; Isabel Gao; et al. (CoRR, 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)

High-performance, Distributed Training of Large-scale Deep Learning Recommendation Models. - Dheevatsa Mudigere; Yuchen Hao; Jianyu Huang; Andrew Tulloch; Srinivas Sridharan 002; Xing Liu; Mustafa Ozdal; Jade Nie; Jongsoo Park; Liang Luo; Jie Amy Yang; Leon Gao; et al. (CoRR, 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 003; Hector Yuen; Shishir Juluri; et al. (CoRR, 2021)

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 003; Hector Yuen; Dheevatsa Mudigere; et al. (ICDCS, 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 002; Xing Liu; Mustafa Ozdal; Jade Nie; Jongsoo Park; Liang Luo; Jie Amy Yang; et al. (ISCA, 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 (NSDI, 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)

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)

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; et al. (ISCA, 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; et al. (MLSys, 2023)

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 (MICRO, 2024)

Parallelization Strategies for DLRM Embedding Bag Operator on AMD CPUs. - Krishnakumar Nair; Avinash-Chandra Pandey; Siddappa Karabannavar; Meena Arunachalam; John Kalamatianos; Varun Agrawal; Saurabh Gupta 002; Ashish Sirasao; Elliott Delaye; Steven K. Reinhardt; Rajesh Vivekanandham; Ralph Wittig; et al. (IEEE Micro, 2024)