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Krishnakumar Nair
Krishnakumar Nair
Facebook, Intel, AMD
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Deep learning training in facebook data centers: Design of scale-up and scale-out systems
M Naumov, J Kim, D Mudigere, S Sridharan, X Wang, W Zhao, S Yilmaz, ...
arXiv preprint arXiv:2003.09518, 2020
802020
Software-hardware co-design for fast and scalable training of deep learning recommendation models
D Mudigere, Y Hao, J Huang, Z Jia, A Tulloch, S Sridharan, X Liu, ...
Proceedings of the 49th Annual International Symposium on Computer …, 2022
692022
M. khorashadi, P
D Mudigere, Y Hao, J Huang, Z Jia, A Tulloch, S Sridharan, X Liu, ...
Bhattacharya, P. Lapukhov, M. Naumov, L. Qiao, M. Smelyanskiy, B. Jia, and V …, 2021
362021
Check-N-Run: A Checkpointing System for Training Deep Learning Recommendation Models
A Eisenman, KK Matam, S Ingram, D Mudigere, R Krishnamoorthi, K Nair, ...
Networked Systems Design and Implementation (NSDI '22 Spring) 19, 2021
352021
High-performance, distributed training of large-scale deep learning recommendation models
D Mudigere, Y Hao, J Huang, A Tulloch, S Sridharan, X Liu, M Ozdal, ...
arXiv preprint arXiv:2104.05158, 2021
302021
Artificial neural network training using flexible floating point tensors
K Nair, A Yang, B Morris
US Patent App. 16/004,243, 2019
232019
Circuit and method for computing depthwise convolution
K Nair, AU Diril, D Mudigere, EKA Zadeh, O Wu, Y Hao
US Patent 11,138,292, 2021
152021
XRBench: An extended reality (XR) machine learning benchmark suite for the metaverse
H Kwon, K Nair, J Seo, J Yik, D Mohapatra, D Zhan, J Song, P Capak, ...
Proceedings of Machine Learning and Systems 5, 2023
142023
Apparatuses and methods to accelerate matrix multiplication
M Urbanski, BJ Hickmann, M Rotzin, K Nair, A Yang, BS Morris, ...
US Patent App. 17/256,195, 2021
142021
Supporting massive DLRM inference through software defined memory
EK Ardestani, C Kim, SJ Lee, L Pan, J Axboe, V Rampersad, B Agrawal, ...
2022 IEEE 42nd International Conference on Distributed Computing Systems …, 2022
122022
Scalable distributed training of recommendation models: An astra-sim+ ns3 case-study with tcp/ip transport
S Rashidi, P Shurpali, S Sridharan, N Hassani, D Mudigere, K Nair, ...
2020 IEEE Symposium on High-Performance Interconnects (HOTI), 33-42, 2020
92020
Deep Learning Training in Facebook Data Centers: Design of Scale-up and Scale-out Systems. arXiv (2020)
M Naumov, J Kim, D Mudigere, S Sridharan, X Wang, W Zhao, S Yilmaz, ...
arXiv preprint arxiv:2003.09518, 2020
72020
Mechanism to perform non-linear functions in a machine learning accelerator
B Daga, K Nair, P Janedula, AB Srinivasan, B Pazhanimala, A Vengallur
US Patent 11,640,537, 2023
62023
Mapping convolution to a partition channel convolution engine
KN Nair, R Komuravelli, AU Diril, EKA Zadeh, Y Hao, M Schatz, TM Ulrich, ...
US Patent 11,520,853, 2022
62022
MTIA: First Generation Silicon Targeting Meta's Recommendation Systems
A Firoozshahian, J Coburn, R Levenstein, R Nattoji, A Kamath, O Wu, ...
Proceedings of the 50th Annual International Symposium on Computer …, 2023
52023
Learning to collide: Recommendation system model compression with learned hash functions
B Ghaemmaghami, M Ozdal, R Komuravelli, D Korchev, D Mudigere, ...
arXiv preprint arXiv:2203.15837, 2022
52022
Circuit and method for calculating non-linear functions of floating-point numbers
AR Kadkol, K Nair
US Patent 11,106,430, 2021
52021
Check-n-run: A checkpointing system for training recommendation models
A Eisenman, KK Matam, S Ingram, D Mudigere, R Krishnamoorthi, ...
arXiv preprint arXiv:2010.08679 5, 2020
52020
Apparatus and method for coherent, accelerated conversion between data representations
K Nair, A Yang, M Rotzin, N Garegrat, T Schebye, T Werner
US Patent 10,761,757, 2020
42020
Hardware for floating-point arithmetic in multiple formats
TM Ulrich, AU Diril, KN Nair, Z Wang, R Komuravelli
US Patent 11,275,560, 2022
32022
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