Milad Hashemi
Milad Hashemi
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Cited by
Cited by
Learning memory access patterns
M Hashemi, K Swersky, J Smith, G Ayers, H Litz, J Chang, C Kozyrakis, ...
International Conference on Machine Learning, 1919-1928, 2018
Two sides of the same coin: Heterophily and oversmoothing in graph convolutional neural networks
Y Yan, M Hashemi, K Swersky, Y Yang, D Koutra
2022 IEEE International Conference on Data Mining (ICDM), 1287-1292, 2022
Morphcore: An energy-efficient microarchitecture for high performance ilp and high throughput tlp
MA Suleman, M Hashemi, C Wilkerson, YN Patt
2012 45th Annual IEEE/ACM International Symposium on Microarchitecture, 305-316, 2012
Accelerating dependent cache misses with an enhanced memory controller
M Hashemi, Khubaib, E Ebrahimi, O Mutlu, YN Patt
ACM SIGARCH Computer Architecture News 44 (3), 444-455, 2016
Continuous runahead: Transparent hardware acceleration for memory intensive workloads
M Hashemi, O Mutlu, YN Patt
2016 49th Annual IEEE/ACM International Symposium on Microarchitecture …, 2016
An imitation learning approach for cache replacement
E Liu, M Hashemi, K Swersky, P Ranganathan, J Ahn
International Conference on Machine Learning, 6237-6247, 2020
Oops i took a gradient: Scalable sampling for discrete distributions
W Grathwohl, K Swersky, M Hashemi, D Duvenaud, C Maddison
International Conference on Machine Learning, 3831-3841, 2021
A hierarchical neural model of data prefetching
Z Shi, A Jain, K Swersky, M Hashemi, P Ranganathan, C Lin
Proceedings of the 26th ACM International Conference on Architectural …, 2021
Neural Execution Engines: Learning to Execute Subroutines
Y Yan, K Swersky, D Koutra, P Ranganathan, M Hashemi
arXiv preprint arXiv:2006.08084, 2020
Filtered runahead execution with a runahead buffer
M Hashemi, YN Patt
Proceedings of the 48th International Symposium on Microarchitecture, 358-369, 2015
Learning execution through neural code fusion
Z Shi, K Swersky, D Tarlow, P Ranganathan, M Hashemi
arXiv preprint arXiv:1906.07181, 2019
Apollo: Transferable architecture exploration
A Yazdanbakhsh, C Angermueller, B Akin, Y Zhou, A Jones, M Hashemi, ...
arXiv preprint arXiv:2102.01723, 2021
Data-driven offline optimization for architecting hardware accelerators
A Kumar, A Yazdanbakhsh, M Hashemi, K Swersky, S Levine
arXiv preprint arXiv:2110.11346, 2021
Learned hardware/software co-design of neural accelerators
Z Shi, C Sakhuja, M Hashemi, K Swersky, C Lin
arXiv preprint arXiv:2010.02075, 2020
Learning performance-improving code edits
A Madaan, A Shypula, U Alon, M Hashemi, P Ranganathan, Y Yang, ...
arXiv preprint arXiv:2302.07867, 2023
Towards better out-of-distribution generalization of neural algorithmic reasoning tasks
S Mahdavi, K Swersky, T Kipf, M Hashemi, C Thrampoulidis, R Liao
arXiv preprint arXiv:2211.00692, 2022
No MCMC for me: Amortized samplers for fast and stable training of energy-based models
D Duvenaud, J Kelly, K Swersky, M Hashemi, M Norouzi, W Grathwohl
Computer system prediction machine learning models
MO Hashemi, P Ranganathan
US Patent App. 15/994,144, 2019
Efficient execution of bursty applications
M Hashemi, D Marr, D Carmean, YN Patt
IEEE Computer Architecture Letters 15 (2), 85-88, 2015
Machine learning for systems
H Litz, M Hashemi
IEEE Micro 40 (05), 6-7, 2020
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