| Deep speech 2: End-to-end speech recognition in english and mandarin D Amodei, S Ananthanarayanan, R Anubhai, J Bai, E Battenberg, C Case, ... International conference on machine learning, 173-182, 2016 | 3191 | 2016 |
| Deep speech: Scaling up end-to-end speech recognition A Hannun, C Case, J Casper, B Catanzaro, G Diamos, E Elsen, ... arXiv preprint arXiv:1412.5567, 2014 | 2231 | 2014 |
| Mixed precision training P Micikevicius, S Narang, J Alben, G Diamos, E Elsen, D Garcia, ... arXiv preprint arXiv:1710.03740, 2017 | 1215 | 2017 |
| Deep voice: Real-time neural text-to-speech SÖ Arık, M Chrzanowski, A Coates, G Diamos, A Gibiansky, Y Kang, X Li, ... International conference on machine learning, 195-204, 2017 | 679 | 2017 |
| Deep learning scaling is predictable, empirically J Hestness, S Narang, N Ardalani, G Diamos, H Jun, H Kianinejad, ... arXiv preprint arXiv:1712.00409, 2017 | 425 | 2017 |
| Deep voice 2: Multi-speaker neural text-to-speech A Gibiansky, S Arik, G Diamos, J Miller, K Peng, W Ping, J Raiman, ... Advances in neural information processing systems 30, 2017 | 353 | 2017 |
| Ocelot: a dynamic optimization framework for bulk-synchronous applications in heterogeneous systems GF Diamos, AR Kerr, S Yalamanchili, N Clark Proceedings of the 19th international conference on Parallel architectures …, 2010 | 333 | 2010 |
| Mlperf inference benchmark VJ Reddi, C Cheng, D Kanter, P Mattson, G Schmuelling, CJ Wu, ... 2020 ACM/IEEE 47th Annual International Symposium on Computer Architecture …, 2020 | 308 | 2020 |
| Exploring sparsity in recurrent neural networks S Narang, E Elsen, G Diamos, S Sengupta arXiv preprint arXiv:1704.05119, 2017 | 294 | 2017 |
| Harmony: an execution model and runtime for heterogeneous many core systems GF Diamos, S Yalamanchili Proceedings of the 17th international symposium on High performance …, 2008 | 249 | 2008 |
| Mlperf training benchmark P Mattson, C Cheng, G Diamos, C Coleman, P Micikevicius, D Patterson, ... Proceedings of Machine Learning and Systems 2, 336-349, 2020 | 241 | 2020 |
| Deep voice 2: Multi-speaker neural text-to-speech S Arik, G Diamos, A Gibiansky, J Miller, K Peng, W Ping, J Raiman, ... arXiv preprint arXiv:1705.08947, 2017 | 187 | 2017 |
| A characterization and analysis of ptx kernels A Kerr, G Diamos, S Yalamanchili 2009 IEEE international symposium on workload characterization (IISWC), 3-12, 2009 | 173 | 2009 |
| Modeling GPU-CPU workloads and systems A Kerr, G Diamos, S Yalamanchili Proceedings of the 3rd workshop on general-purpose computation on graphics …, 2010 | 146 | 2010 |
| Simultaneous branch and warp interweaving for sustained GPU performance N Brunie, C Collange, G Diamos ACM SIGARCH Computer Architecture News 40 (3), 49-60, 2012 | 134 | 2012 |
| MLPerf: An industry standard benchmark suite for machine learning performance P Mattson, VJ Reddi, C Cheng, C Coleman, G Diamos, D Kanter, ... IEEE Micro 40 (2), 8-16, 2020 | 121 | 2020 |
| Kernel weaver: Automatically fusing database primitives for efficient gpu computation H Wu, G Diamos, S Cadambi, S Yalamanchili 2012 45th Annual IEEE/ACM International Symposium on Microarchitecture, 107-118, 2012 | 121 | 2012 |
| Block-sparse recurrent neural networks S Narang, E Undersander, G Diamos arXiv preprint arXiv:1711.02782, 2017 | 119 | 2017 |
| SIMD re-convergence at thread frontiers G Diamos, B Ashbaugh, S Maiyuran, A Kerr, H Wu, S Yalamanchili Proceedings of the 44th annual IEEE/ACM international symposium on …, 2011 | 109 | 2011 |
| Persistent rnns: Stashing recurrent weights on-chip G Diamos, S Sengupta, B Catanzaro, M Chrzanowski, A Coates, E Elsen, ... International Conference on Machine Learning, 2024-2033, 2016 | 103 | 2016 |
