Gregory Diamos
Gregory Diamos
Landing AI
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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
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
Mixed precision training
P Micikevicius, S Narang, J Alben, G Diamos, E Elsen, D Garcia, ...
arXiv preprint arXiv:1710.03740, 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
Deep learning scaling is predictable, empirically
J Hestness, S Narang, N Ardalani, G Diamos, H Jun, H Kianinejad, ...
arXiv preprint arXiv:1712.00409, 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
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
Exploring sparsity in recurrent neural networks
S Narang, E Elsen, G Diamos, S Sengupta
arXiv preprint arXiv:1704.05119, 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
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
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
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
A characterization and analysis of ptx kernels
A Kerr, G Diamos, S Yalamanchili
2009 IEEE international symposium on workload characterization (IISWC), 3-12, 2009
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
Modeling GPU-CPU workloads and systems
A Kerr, G Diamos, S Yalamanchili
Proceedings of the 3rd workshop on general-purpose computation on graphics …, 2010
Block-sparse recurrent neural networks
S Narang, E Undersander, G Diamos
arXiv preprint arXiv:1711.02782, 2017
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
Fast spectrogram inversion using multi-head convolutional neural networks
SÖ Arık, H Jun, G Diamos
IEEE Signal Processing Letters 26 (1), 94-98, 2018
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
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
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