| The gem5 simulator N Binkert, B Beckmann, G Black, SK Reinhardt, A Saidi, A Basu, ... ACM SIGARCH computer architecture news 39 (2), 1-7, 2011 | 6244 | 2011 |
| Deep learning scaling is predictable, empirically J Hestness, S Narang, N Ardalani, G Diamos, H Jun, H Kianinejad, ... arXiv preprint arXiv:1712.00409, 2017 | 819 | 2017 |
| gem5-gpu: A heterogeneous cpu-gpu simulator J Power, J Hestness, MS Orr, MD Hill, DA Wood IEEE Computer Architecture Letters 14 (1), 34-36, 2014 | 338 | 2014 |
| Kilo-NOC: a heterogeneous network-on-chip architecture for scalability and service guarantees B Grot, J Hestness, SW Keckler, O Mutlu ACM SIGARCH computer architecture news 39 (3), 401-412, 2011 | 291 | 2011 |
| Express cube topologies for on-chip interconnects B Grot, J Hestness, SW Keckler, O Mutlu 2009 IEEE 15th International Symposium on High Performance Computer …, 2009 | 269 | 2009 |
| Netrace: dependency-driven trace-based network-on-chip simulation J Hestness, B Grot, SW Keckler Proceedings of the Third International Workshop on Network on Chip …, 2010 | 179 | 2010 |
| Convolutional Recurrent Neural Networks for Small-Footprint Keyword Spotting SO Arik, M Kliegl, R Child, J Hestness, A Gibiansky, C Fougner, ... Interspeech 2017, 2017 | 178 | 2017 |
| Compositional generalization for primitive substitutions Y Li, L Zhao, J Wang, J Hestness arXiv preprint arXiv:1910.02612, 2019 | 104 | 2019 |
| SlimPajama: A 627B token cleaned and deduplicated version of RedPajama D Soboleva, F Al-Khateeb, R Myers, JR Steeves, J Hestness, N Dey June, 2023 | 101 | 2023 |
| Running PARSEC 2.1 on M5 M Gebhart, J Hestness, E Fatehi, P Gratz, SW Keckler The University of Texas at Austin, Department of Computer Science, Tech. Rep, 2009 | 96 | 2009 |
| Convolutional recurrent neural networks for small-footprint keyword spotting S Arik, M Kliegl, R Child, J Hestness, A Gibiansky, C Fougner, R Prenger, ... US Patent 10,540,961, 2020 | 95 | 2020 |
| Beyond human-level accuracy: Computational challenges in deep learning J Hestness, N Ardalani, G Diamos Proceedings of the 24th symposium on principles and practice of parallel …, 2019 | 91 | 2019 |
| The Gem5 Simulator. SIGARCH Comput. Archit. News 39, 2 (Aug. 2011), 1–7 N Binkert, B Beckmann, G Black, SK Reinhardt, A Saidi, A Basu, ... | 82 | 2011 |
| A comparative analysis of microarchitecture effects on CPU and GPU memory system behavior J Hestness, SW Keckler, DA Wood 2014 IEEE International Symposium on Workload Characterization (IISWC), 150-160, 2014 | 79 | 2014 |
| Cerebras-gpt: Open compute-optimal language models trained on the cerebras wafer-scale cluster N Dey, G Gosal, H Khachane, W Marshall, R Pathria, M Tom, J Hestness arXiv preprint arXiv:2304.03208, 2023 | 73 | 2023 |
| Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models N Sengupta, SK Sahu, B Jia, S Katipomu, H Li, F Koto, W Marshall, ... arXiv preprint arXiv:2308.16149, 2023 | 72 | 2023 |
| GPU computing pipeline inefficiencies and optimization opportunities in heterogeneous CPU-GPU processors J Hestness, SW Keckler, DA Wood 2015 IEEE International Symposium on Workload Characterization, 87-97, 2015 | 67 | 2015 |
| Netrace: Dependency-tracking traces for efficient network-on-chip experimentation J Hestness, SW Keckler The University of Texas at Austin, Dept. of Computer Science, Tech. Rep, 2011 | 56 | 2011 |
| Slimpajama-dc: Understanding data combinations for llm training Z Shen, T Tao, L Ma, W Neiswanger, Z Liu, H Wang, B Tan, J Hestness, ... arXiv preprint arXiv:2309.10818, 2023 | 37 | 2023 |
| Pipelined backpropagation at scale: training large models without batches A Kosson, V Chiley, A Venigalla, J Hestness, U Koster Proceedings of Machine Learning and Systems 3, 479-501, 2021 | 35 | 2021 |
David A. WoodUniversity of Wisconsin, MadisonVerified email at cs.wisc.edu
