| 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 | 491 | 2020 |
| 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 | 387 | 2019 |
| 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 | 297 | 2018 |
| 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 | 283 | 2017 |
| 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 | 215 | 2018 |
| 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 | 215 | 2016 |
| Method and apparatus to manage network addresses B Kaul, N Tulpule, M Zhu, P Krishnaswamy US Patent App. 10/651,929, 2005 | 204 | 2005 |
| 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 |
| Mixed precision training with 8-bit floating point N Mellempudi, S Srinivasan, D Das, B Kaul arXiv preprint arXiv:1905.12334, 2019 | 81 | 2019 |
| Apparatuses, methods, and systems for neural networks S Venkataramani, D Das, A Ranjan, S Banerjee, S Avancha, ... US Patent App. 16/317,497, 2019 | 61 | 2019 |
| Data structure and movement for lattice-based simulations AG Shet, SH Sorathiya, S Krithivasan, AM Deshpande, B Kaul, ... Physical Review E—Statistical, Nonlinear, and Soft Matter Physics 88 (1 …, 2013 | 54 | 2013 |
| 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 | 47 | 2019 |
| 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 |
| Madras: Multi agent driving simulator A Santara, S Rudra, SA Buridi, M Kaushik, A Naik, B Kaul, B Ravindran Journal of Artificial Intelligence Research 70, 1517-1555, 2021 | 33 | 2021 |
| Manna: An accelerator for memory-augmented neural networks JR Stevens, A Ranjan, D Das, B Kaul, A Raghunathan Proceedings of the 52nd Annual IEEE/ACM International Symposium on …, 2019 | 31 | 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 | 31 | 2017 |
| PolyDL: Polyhedral optimizations for creation of high-performance dl primitives S Tavarageri, A Heinecke, S Avancha, B Kaul, G Goyal, R Upadrasta ACM Transactions on Architecture and Code Optimization (TACO) 18 (1), 1-27, 2021 | 29 | 2021 |
| AI Powered Compiler Techniques for DL Code Optimization S Tavarageri, G Goyal, S Avancha, B Kaul, R Upadrasta arXiv preprint arXiv:2104.05573, 2021 | 26 | 2021 |
| Rail: Risk-averse imitation learning A Santara, A Naik, B Ravindran, D Das, D Mudigere, S Avancha, B Kaul arXiv preprint arXiv:1707.06658, 2017 | 26 | 2017 |
| GNNerator: A hardware/software framework for accelerating graph neural networks JR Stevens, D Das, S Avancha, B Kaul, A Raghunathan 2021 58th ACM/IEEE Design Automation Conference (DAC), 955-960, 2021 | 24 | 2021 |
Dipankar DasIntel Parallel Computing Labs, Intel Labsintel.com의 이메일 확인됨