Thierry Moreau
Thierry Moreau
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{TVM}: An automated {End-to-End} optimizing compiler for deep learning
T Chen, T Moreau, Z Jiang, L Zheng, E Yan, H Shen, M Cowan, L Wang, ...
13th USENIX Symposium on Operating Systems Design and Implementation (OSDI …, 2018
Learning to optimize tensor programs
T Chen, L Zheng, E Yan, Z Jiang, T Moreau, L Ceze, C Guestrin, ...
Advances in Neural Information Processing Systems 31, 2018
TVM: end-to-end optimization stack for deep learning
T Chen, T Moreau, Z Jiang, H Shen, EQ Yan, L Wang, Y Hu, L Ceze, ...
arXiv preprint arXiv:1802.04799 11 (2018), 20, 2018
SNNAP: Approximate computing on programmable SoCs via neural acceleration
T Moreau, M Wyse, J Nelson, A Sampson, H Esmaeilzadeh, L Ceze, ...
2015 IEEE 21st International Symposium on High Performance Computer …, 2015
A hardware–software blueprint for flexible deep learning specialization
T Moreau, T Chen, L Vega, J Roesch, E Yan, L Zheng, J Fromm, Z Jiang, ...
IEEE Micro 39 (5), 8-16, 2019
Accept: A programmer-guided compiler framework for practical approximate computing
A Sampson, A Baixo, B Ransford, T Moreau, J Yip, L Ceze, M Oskin
University of Washington Technical Report UW-CSE-15-01 1 (2), 1-14, 2015
VTA: an open hardware-software stack for deep learning
T Moreau, T Chen, Z Jiang, L Ceze, C Guestrin, A Krishnamurthy
arXiv preprint arXiv:1807.04188 10, 2018
Exploiting errors for efficiency: A survey from circuits to applications
P Stanley-Marbell, A Alaghi, M Carbin, E Darulova, L Dolecek, ...
ACM Computing Surveys (CSUR) 53 (3), 1-39, 2020
A taxonomy of general purpose approximate computing techniques
T Moreau, J San Miguel, M Wyse, J Bornholt, A Alaghi, L Ceze, NE Jerger, ...
IEEE Embedded Systems Letters 10 (1), 2-5, 2017
MATIC: Learning around errors for efficient low-voltage neural network accelerators
S Kim, P Howe, T Moreau, A Alaghi, L Ceze, V Sathe
2018 Design, Automation & Test in Europe Conference & Exhibition (DATE), 1-6, 2018
Energy-efficient neural network acceleration in the presence of bit-level memory errors
S Kim, P Howe, T Moreau, A Alaghi, L Ceze, VS Sathe
IEEE Transactions on Circuits and Systems I: Regular Papers 65 (12), 4285-4298, 2018
Automatic generation of high-performance quantized machine learning kernels
M Cowan, T Moreau, T Chen, J Bornholt, L Ceze
Proceedings of the 18th ACM/IEEE International Symposium on Code Generation …, 2020
Approximate computing: Making mobile systems more efficient
T Moreau, A Sampson, L Ceze
IEEE Pervasive Computing 14 (2), 9-13, 2015
Relay: A high-level compiler for deep learning
J Roesch, S Lyubomirsky, M Kirisame, L Weber, J Pollock, L Vega, ...
arXiv preprint arXiv:1904.08368, 2019
Leveraging the vta-tvm hardware-software stack for fpga acceleration of 8-bit resnet-18 inference
T Moreau, T Chen, L Ceze
Proceedings of the 1st on Reproducible Quality-Efficient Systems Tournament …, 2018
Exploring computation-communication tradeoffs in camera systems
A Mazumdar, T Moreau, S Kim, M Cowan, A Alaghi, L Ceze, M Oskin, ...
2017 IEEE International Symposium on Workload Characterization (IISWC), 177-186, 2017
Automating generation of low precision deep learning operators
M Cowan, T Moreau, T Chen, L Ceze
arXiv preprint arXiv:1810.11066, 2018
React: A framework for rapid exploration of approximate computing techniques
M Wyse, A Baixo, T Moreau, B Zorn, J Bornholt, A Sampson, L Ceze, ...
Workshop on Approximate Computing Across the Stack (WAX w/PLDI), 7-9, 2015
QAPPA: A framework for navigating quality-energy tradeoffs with arbitrary quantization
T Moreau, F Augusto, P Howe, A Alaghi, L Ceze
Technical Report UW-CSE-17-03-02, 2017
Exploiting quality-energy tradeoffs with arbitrary quantization: special session paper
T Moreau, F Augusto, P Howe, A Alaghi, L Ceze
Proceedings of the Twelfth IEEE/ACM/IFIP International Conference on …, 2017
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