| Graphmat: High performance graph analytics made productive N Sundaram, NR Satish, MMA Patwary, SR Dulloor, SG Vadlamudi, ... arXiv preprint arXiv:1503.07241, 2015 | 304 | 2015 |
| Scaledeep: A scalable compute architecture for learning and evaluating deep networks S Venkataramani, A Ranjan, S Banerjee, D Das, S Avancha, ... Proceedings of the 44th Annual International Symposium on Computer …, 2017 | 212 | 2017 |
| Sigma: A sparse and irregular gemm accelerator with flexible interconnects for dnn training E Qin, A Samajdar, H Kwon, V Nadella, S Srinivasan, D Das, B Kaul, ... 2020 IEEE International Symposium on High Performance Computer Architecture …, 2020 | 186 | 2020 |
| Distributed deep learning using synchronous stochastic gradient descent D Das, S Avancha, D Mudigere, K Vaidynathan, S Sridharan, D Kalamkar, ... arXiv preprint arXiv:1602.06709, 2016 | 181 | 2016 |
| Reconfigurable interface-based electrical architecture D Das, VK Agrawal, S Rajappan US Patent 8,930,036, 2015 | 181 | 2015 |
| Out-of-distribution detection using an ensemble of self supervised leave-out classifiers A Vyas, N Jammalamadaka, X Zhu, D Das, B Kaul, TL Willke Proceedings of the European Conference on Computer Vision (ECCV), 550-564, 2018 | 166 | 2018 |
| 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 | 142 | 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 | 135 | 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 | 105 | 2017 |
| Parallel efficient sparse matrix-matrix multiplication on multicore platforms M Patwary, M Ali, NR Satish, N Sundaram, J Park, MJ Anderson, ... International Conference on High Performance Computing, 48-57, 2015 | 66 | 2015 |
| Improving concurrency and asynchrony in multithreaded MPI applications using software offloading K Vaidyanathan, DD Kalamkar, K Pamnany, JR Hammond, P Balaji, ... SC'15: Proceedings of the International Conference for High Performance …, 2015 | 51 | 2015 |
| Mixed precision training with 8-bit floating point N Mellempudi, S Srinivasan, D Das, B Kaul arXiv preprint arXiv:1905.12334, 2019 | 40 | 2019 |
| Abstraction layers for scalable distributed machine learning DD Kalamkar, K Vaidyanathan, S Sridharan, D Das US Patent 11,094,029, 2021 | 28 | 2021 |
| 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 | 28 | 2018 |
| Apparatuses, methods, and systems for neural networks S Venkataramani, D Das, A Ranjan, S Banerjee, S Avancha, ... US Patent App. 16/317,497, 2019 | 26 | 2019 |
| 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 | 25 | 2017 |
| Dictionary based code compression for variable length instruction encodings D Das, R Kumar, PP Chakrabarti 18th International Conference on VLSI Design held jointly with 4th …, 2005 | 24 | 2005 |
| X-mann: A crossbar based architecture for memory augmented neural networks A Ranjan, S Jain, JR Stevens, D Das, B Kaul, A Raghunathan Proceedings of the 56th Annual Design Automation Conference 2019, 1-6, 2019 | 22 | 2019 |
| Methods and systems for diagnosing hardware and software faults using time-stamped events P Sinha, D Das US Patent 8,464,102, 2013 | 22 | 2013 |
| Communication optimizations for distributed machine learning S Sridharan, K Vaidyanathan, D Das, C Sakthivel, ME Smorkalov US Patent 11,270,201, 2022 | 20 | 2022 |
Naveen MellempudiResearch Scientist, Intel CorporationVerified email at intel.com
