| Deep learning with limited numerical precision S Gupta, A Agrawal, K Gopalakrishnan, P Narayanan International Conference on Machine Learning, 1737-1746, 2015 | 2539 | 2015 |
| To prune, or not to prune: exploring the efficacy of pruning for model compression M Zhu, S Gupta arXiv preprint arXiv:1710.01878, 2017 | 1333 | 2017 |
| Achieving direct band gap in germanium through integration of Sn alloying and external strain S Gupta, B Magyari-Köpe, Y Nishi, KC Saraswat Journal of Applied Physics 113 (7), 2013 | 493 | 2013 |
| Staleness-aware Async-SGD for Distributed Deep Learning W Zhang, S Gupta, X Lian, J Liu arXiv preprint arXiv:1511.05950, 2015 | 315 | 2015 |
| Model accuracy and runtime tradeoff in distributed deep learning: A systematic study S Gupta, W Zhang, F Wang 2016 IEEE 16th International Conference on Data Mining (ICDM), 171-180, 2016 | 224 | 2016 |
| Lingvo: a Modular and Scalable Framework for Sequence-to-Sequence Modeling J Shen, P Nguyen, Y Wu, Z Chen, MX Chen, Y Jia, A Kannan, T Sainath, ... arXiv preprint arXiv:1902.08295, 2019 | 202 | 2019 |
| Mobiledets: Searching for object detection architectures for mobile accelerators Y Xiong, H Liu, S Gupta, B Akin, G Bender, Y Wang, PJ Kindermans, ... Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2021 | 150 | 2021 |
| GeSn technology: Extending the Ge electronics roadmap S Gupta, R Chen, B Magyari-Kope, H Lin, B Yang, A Nainani, Y Nishi, ... Electron Devices Meeting (IEDM), 2011 IEEE International, 16.6. 1-16.6. 4, 2011 | 135 | 2011 |
| Demonstration of a Ge/GeSn/Ge quantum-well microdisk resonator on silicon: enabling high-quality Ge (Sn) materials for micro-and nanophotonics R Chen, S Gupta, YC Huang, Y Huo, CW Rudy, E Sanchez, Y Kim, ... Nano letters 14 (1), 37-43, 2014 | 134 | 2014 |
| Approximate computing: Challenges and opportunities A Agrawal, J Choi, K Gopalakrishnan, S Gupta, R Nair, J Oh, DA Prener, ... 2016 IEEE International Conference on Rebooting Computing (ICRC), 1-8, 2016 | 120 | 2016 |
| Highly Selective Dry Etching of Germanium over Germanium–Tin (Ge1–x Sn x): A Novel Route for Ge1–x Sn x Nanostructure Fabrication S Gupta, R Chen, YC Huang, Y Kim, E Sanchez, JS Harris, KC Saraswat Nano letters 13 (8), 3783-3790, 2013 | 109 | 2013 |
| Theoretical analysis of GeSn alloys as a gain medium for a Si-compatible laser B Dutt, H Lin, DS Sukhdeo, BM Vulovic, S Gupta, D Nam, KC Saraswat, ... IEEE Journal of Selected Topics in Quantum Electronics 19 (5), 1502706-1502706, 2013 | 102 | 2013 |
| 7-nm FinFET CMOS Design Enabled by Stress Engineering Using Si, Ge, and Sn S Gupta, V Moroz, L Smith, Q Lu, KC Saraswat IEEE, 2014 | 99 | 2014 |
| Material characterization of high Sn-content, compressively-strained GeSn epitaxial films after rapid thermal processing R Chen, YC Huang, S Gupta, AC Lin, E Sanchez, Y Kim, KC Saraswat, ... Journal of Crystal Growth 365, 29-34, 2013 | 97 | 2013 |
| Hole Mobility Enhancement in Compressively Strained Ge0. 93Sn0. 07 pMOSFETs S Gupta, YC Huang, Y Kim, E Sanchez, KC Saraswat IEEE ELECTRON DEVICE LETTERS 34 (7), 831, 2013 | 90 | 2013 |
| Accelerator-aware Neural Network Design using AutoML S Gupta, B Akin arXiv preprint arXiv:2003.02838, 2020 | 81 | 2020 |
| New materials for post-Si computing: Ge and GeSn devices S Gupta, X Gong, R Zhang, YC Yeo, S Takagi, KC Saraswat MRS Bulletin 39 (08), 678-686, 2014 | 66 | 2014 |
| Towards deep learning using tensorflow lite on risc-v MS Louis, Z Azad, L Delshadtehrani, S Gupta, P Warden, VJ Reddi, ... Third Workshop on Computer Architecture Research with RISC-V (CARRV) 1, 6, 2019 | 61 | 2019 |
| Compression of End-to-End Models. R Pang, TN Sainath, R Prabhavalkar, S Gupta, Y Wu, S Zhang, CC Chiu Interspeech, 27-31, 2018 | 50 | 2018 |
| EfficientNet-EdgeTPU: Creating accelerator-optimized neural networks with AutoML S Gupta, M Tan Google AI Blog 2 (1), 2019 | 49 | 2019 |
Krishna SaraswatProfessor of Electrical Engineering, Stanford University在 stanford.edu 的电子邮件经过验证
