| 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 | 284 | 2022 |
| 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 | 253 | 2018 |
| 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 | 138* | 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 | 132 | 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 | 124 | 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 | 92 | 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 | 90 | 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 | 86 | 2021 |
| Learning performance-improving code edits A Shypula, A Madaan, Y Zeng, U Alon, J Gardner, M Hashemi, G Neubig, ... arXiv preprint arXiv:2302.07867, 2023 | 66 | 2023 |
| Neural Execution Engines: Learning to Execute Subroutines Y Yan, K Swersky, D Koutra, P Ranganathan, M Hashemi arXiv preprint arXiv:2006.08084, 2020 | 47 | 2020 |
| Filtered runahead execution with a runahead buffer M Hashemi, YN Patt Proceedings of the 48th International Symposium on Microarchitecture, 358-369, 2015 | 40 | 2015 |
| Learning execution through neural code fusion Z Shi, K Swersky, D Tarlow, P Ranganathan, M Hashemi arXiv preprint arXiv:1906.07181, 2019 | 36 | 2019 |
| Data-driven offline optimization for architecting hardware accelerators A Kumar, A Yazdanbakhsh, M Hashemi, K Swersky, S Levine arXiv preprint arXiv:2110.11346, 2021 | 34 | 2021 |
| Apollo: Transferable architecture exploration A Yazdanbakhsh, C Angermueller, B Akin, Y Zhou, A Jones, M Hashemi, ... arXiv preprint arXiv:2102.01723, 2021 | 29* | 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 | 22* | 2020 |
| 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 | 20 | 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 International Conference on Learning Representations (ICLR), 2021 | 11 | 2021 |
| Computer system prediction machine learning models MO Hashemi, P Ranganathan US Patent App. 15/994,144, 2019 | 10 | 2019 |
| Cuf: Continuous upsampling filters CN Vasconcelos, C Oztireli, M Matthews, M Hashemi, K Swersky, ... Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2023 | 7 | 2023 |
| Learning to improve code efficiency B Chen, D Tarlow, K Swersky, M Maas, P Heiber, A Naik, M Hashemi, ... arXiv preprint arXiv:2208.05297, 2022 | 5 | 2022 |
