| A study of BFLOAT16 for deep learning training D Kalamkar, D Mudigere, N Mellempudi, D Das, K Banerjee, S Avancha, ... arXiv preprint arXiv:1905.12322, 2019 | 349 | 2019 |
| Mixed precision training of convolutional neural networks using integer operations D Das, N Mellempudi, D Mudigere, D Kalamkar, S Avancha, K Banerjee, ... arXiv preprint arXiv:1802.00930, 2018 | 200 | 2018 |
| Ternary neural networks with fine-grained quantization N Mellempudi, A Kundu, D Mudigere, D Das, B Kaul, P Dubey arXiv preprint arXiv:1705.01462, 2017 | 140 | 2017 |
| Performing power management in a multicore processor VW Lee, ET Grochowski, D Kim, Y Bai, S Li, NK Mellempudi, ... US Patent 10,234,930, 2019 | 129 | 2019 |
| Fp8 formats for deep learning P Micikevicius, D Stosic, N Burgess, M Cornea, P Dubey, R Grisenthwaite, ... arXiv preprint arXiv:2209.05433, 2022 | 115 | 2022 |
| Mixed precision training with 8-bit floating point N Mellempudi, S Srinivasan, D Das, B Kaul arXiv preprint arXiv:1905.12334, 2019 | 77 | 2019 |
| Dynamic precision management for integer deep learning primitives N Mellempudi, D Mudigere, D Das, S Sridharan US Patent 10,643,297, 2020 | 50 | 2020 |
| Optimized compute hardware for machine learning operations D Das, R Gramunt, M Smelyanskiy, J Corbal, D Mudigere, NK Mellempudi, ... US Patent 10,776,699, 2020 | 47 | 2020 |
| Scaling half-precision floating point tensors for training deep neural networks N Mellempudi, D Das US Patent 11,501,139, 2022 | 46 | 2022 |
| On scale-out deep learning training for cloud and hpc S Sridharan, K Vaidyanathan, D Kalamkar, D Das, ME Smorkalov, ... arXiv preprint arXiv:1801.08030, 2018 | 35 | 2018 |
| Mixed low-precision deep learning inference using dynamic fixed point N Mellempudi, A Kundu, D Das, D Mudigere, B Kaul arXiv preprint arXiv:1701.08978, 2017 | 29 | 2017 |
| Performing power management in a multicore processor VW Lee, D Kim, Y Bai, S Ji, S Li, DD Kalamkar, NK Mellempudi US Patent 9,910,481, 2018 | 24 | 2018 |
| Incremental precision networks using residual inference and fine-grain quantization A Kundu, N Mellempudi, D Mudigere, D Das US Patent 11,556,772, 2023 | 21 | 2023 |
| Conversion hardware mechanism N Mellempudi, D Das, MEI Chunhui, K Wong, DD Kalamkar, HH Jiang, ... US Patent 11,494,163, 2022 | 15 | 2022 |
| Ternary residual networks A Kundu, K Banerjee, N Mellempudi, D Mudigere, D Das, B Kaul, ... arXiv preprint arXiv:1707.04679, 2017 | 15 | 2017 |
| Dynamic precision management for integer deep learning primitives N Mellempudi, D Mudigere, D Das, S Sridharan US Patent 11,321,805, 2022 | 10 | 2022 |
| Efficient post-training quantization with fp8 formats H Shen, N Mellempudi, X He, Q Gao, C Wang, M Wang Proceedings of Machine Learning and Systems 6, 483-498, 2024 | 8 | 2024 |
| Technologies for scaling deep learning training NK Mellempudi, S Sridharan, D Mudigere, D Das US Patent 11,068,780, 2021 | 8 | 2021 |
| High performance scalable FPGA accelerator for deep neural networks S Srinivasan, P Janedula, S Dhoble, S Avancha, D Das, N Mellempudi, ... arXiv preprint arXiv:1908.11809, 2019 | 5 | 2019 |
| Performing power management in a multicore processor VW Lee, ET Grochowski, D Kim, Y Bai, S Li, NK Mellempudi, ... US Patent 10,775,873, 2020 | 4 | 2020 |
Dipankar DasIntel Parallel Computing Labs, Intel LabsVerified email at intel.com
