| 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 | 3849 | 2016 |
| Deep Speech: Scaling up end-to-end speech recognition A Hannun arXiv preprint arXiv:1412.5567, 2014 | 2752 | 2014 |
| Mixed precision training P Micikevicius, S Narang, J Alben, G Diamos, E Elsen, D Garcia, ... arXiv preprint arXiv:1710.03740, 2017 | 1971 | 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 | 847 | 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 | 785 | 2017 |
| 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 | 554 | 2020 |
| 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 | 443 | 2017 |
| Exploring sparsity in recurrent neural networks S Narang, E Elsen, G Diamos, S Sengupta arXiv preprint arXiv:1704.05119, 2017 | 374 | 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 | 348 | 2010 |
| 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 | 342 | 2020 |
| 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 | 250 | 2008 |
| 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 | 226 | 2017 |
| 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 | 183 | 2020 |
| A characterization and analysis of ptx kernels A Kerr, G Diamos, S Yalamanchili 2009 IEEE international symposium on workload characterization (IISWC), 3-12, 2009 | 182 | 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 | 160 | 2010 |
| Block-sparse recurrent neural networks S Narang, E Undersander, G Diamos arXiv preprint arXiv:1711.02782, 2017 | 158 | 2017 |
| Fast spectrogram inversion using multi-head convolutional neural networks SÖ Arık, H Jun, G Diamos IEEE Signal Processing Letters 26 (1), 94-98, 2018 | 142 | 2018 |
| 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 | 142 | 2012 |
| 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 | 137 | 2012 |
| Dataperf: Benchmarks for data-centric ai development M Mazumder, C Banbury, X Yao, B Karlaš, W Gaviria Rojas, S Diamos, ... Advances in Neural Information Processing Systems 36, 2024 | 128 | 2024 |
