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Milad Hashemi
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Journal Articles
- 2023
- [j3]Sadegh Mahdavi, Kevin Swersky, Thomas Kipf, Milad Hashemi, Christos Thrampoulidis, Renjie Liao:
Towards Better Out-of-Distribution Generalization of Neural Algorithmic Reasoning Tasks. Trans. Mach. Learn. Res. 2023 (2023) - 2020
- [j2]Heiner Litz, Milad Hashemi:
Machine Learning for Systems. IEEE Micro 40(5): 6-7 (2020) - 2016
- [j1]Milad Hashemi, Debbie Marr, Doug Carmean, Yale N. Patt:
Efficient Execution of Bursty Applications. IEEE Comput. Archit. Lett. 15(2): 85-88 (2016)
Conference and Workshop Papers
- 2024
- [c16]Akanksha Jain
, Hannah Lin
, Carlos Villavieja
, Baris Kasikci
, Chris Kennelly
, Milad Hashemi
, Parthasarathy Ranganathan
:
Limoncello: Prefetchers for Scale. ASPLOS (3) 2024: 577-590 - [c15]Alexander Shypula, Aman Madaan, Yimeng Zeng, Uri Alon, Jacob R. Gardner, Yiming Yang, Milad Hashemi, Graham Neubig, Parthasarathy Ranganathan, Osbert Bastani, Amir Yazdanbakhsh:
Learning Performance-Improving Code Edits. ICLR 2024 - 2023
- [c14]Cristina Nader Vasconcelos, A. Cengiz Öztireli, Mark J. Matthews, Milad Hashemi, Kevin Swersky, Andrea Tagliasacchi:
CUF: Continuous Upsampling Filters. CVPR 2023: 9999-10008 - 2022
- [c13]Yujun Yan
, Milad Hashemi, Kevin Swersky, Yaoqing Yang, Danai Koutra:
Two Sides of the Same Coin: Heterophily and Oversmoothing in Graph Convolutional Neural Networks. ICDM 2022: 1287-1292 - [c12]Aviral Kumar, Amir Yazdanbakhsh, Milad Hashemi, Kevin Swersky, Sergey Levine:
Data-Driven Offline Optimization for Architecting Hardware Accelerators. ICLR 2022 - 2021
- [c11]Zhan Shi, Akanksha Jain, Kevin Swersky, Milad Hashemi, Parthasarathy Ranganathan, Calvin Lin:
A hierarchical neural model of data prefetching. ASPLOS 2021: 861-873 - [c10]Will Sussman Grathwohl, Jacob Jin Kelly, Milad Hashemi, Mohammad Norouzi, Kevin Swersky, David Duvenaud:
No MCMC for me: Amortized sampling for fast and stable training of energy-based models. ICLR 2021 - [c9]Will Grathwohl, Kevin Swersky, Milad Hashemi, David Duvenaud, Chris J. Maddison:
Oops I Took A Gradient: Scalable Sampling for Discrete Distributions. ICML 2021: 3831-3841 - 2020
- [c8]Zhan Shi, Kevin Swersky, Daniel Tarlow, Parthasarathy Ranganathan, Milad Hashemi:
Learning Execution through Neural Code fusion. ICLR 2020 - [c7]Evan Zheran Liu, Milad Hashemi, Kevin Swersky, Parthasarathy Ranganathan, Junwhan Ahn:
An Imitation Learning Approach for Cache Replacement. ICML 2020: 6237-6247 - [c6]Yujun Yan, Kevin Swersky, Danai Koutra, Parthasarathy Ranganathan, Milad Hashemi:
Neural Execution Engines: Learning to Execute Subroutines. NeurIPS 2020 - 2018
- [c5]Milad Hashemi, Kevin Swersky, Jamie A. Smith, Grant Ayers, Heiner Litz, Jichuan Chang, Christos Kozyrakis, Parthasarathy Ranganathan:
Learning Memory Access Patterns. ICML 2018: 1924-1933 - 2016
- [c4]Milad Hashemi, Khubaib, Eiman Ebrahimi, Onur Mutlu, Yale N. Patt:
Accelerating Dependent Cache Misses with an Enhanced Memory Controller. ISCA 2016: 444-455 - [c3]Milad Hashemi, Onur Mutlu, Yale N. Patt:
Continuous runahead: Transparent hardware acceleration for memory intensive workloads. MICRO 2016: 61:1-61:12 - 2015
- [c2]Milad Hashemi, Yale N. Patt:
Filtered runahead execution with a runahead buffer. MICRO 2015: 358-369 - 2012
- [c1]Khubaib, M. Aater Suleman, Milad Hashemi, Chris Wilkerson, Yale N. Patt:
MorphCore: An Energy-Efficient Microarchitecture for High Performance ILP and High Throughput TLP. MICRO 2012: 305-316
Informal and Other Publications
- 2023
- [i16]Aman Madaan, Alexander Shypula, Uri Alon, Milad Hashemi, Parthasarathy Ranganathan, Yiming Yang, Graham Neubig, Amir Yazdanbakhsh:
Learning Performance-Improving Code Edits. CoRR abs/2302.07867 (2023) - 2022
- [i15]Binghong Chen, Daniel Tarlow, Kevin Swersky, Martin Maas, Pablo Ariel Heiber, Ashish Naik, Milad Hashemi, Parthasarathy Ranganathan:
Learning to Improve Code Efficiency. CoRR abs/2208.05297 (2022) - [i14]Cristina Nader Vasconcelos, Cengiz Öztireli, Mark J. Matthews, Milad Hashemi, Kevin Swersky, Andrea Tagliasacchi:
CUF: Continuous Upsampling Filters. CoRR abs/2210.06965 (2022) - [i13]Sadegh Mahdavi, Kevin Swersky, Thomas Kipf, Milad Hashemi, Christos Thrampoulidis, Renjie Liao:
Towards Better Out-of-Distribution Generalization of Neural Algorithmic Reasoning Tasks. CoRR abs/2211.00692 (2022) - 2021
- [i12]Amir Yazdanbakhsh, Christof Angermüller, Berkin Akin, Yanqi Zhou, Albin Jones, Milad Hashemi, Kevin Swersky, Satrajit Chatterjee, Ravi Narayanaswami, James Laudon:
Apollo: Transferable Architecture Exploration. CoRR abs/2102.01723 (2021) - [i11]Will Grathwohl, Kevin Swersky, Milad Hashemi, David Duvenaud, Chris J. Maddison:
Oops I Took A Gradient: Scalable Sampling for Discrete Distributions. CoRR abs/2102.04509 (2021) - [i10]Yujun Yan, Milad Hashemi, Kevin Swersky, Yaoqing Yang, Danai Koutra:
Two Sides of the Same Coin: Heterophily and Oversmoothing in Graph Convolutional Neural Networks. CoRR abs/2102.06462 (2021) - [i9]Aviral Kumar, Amir Yazdanbakhsh, Milad Hashemi, Kevin Swersky, Sergey Levine:
Data-Driven Offline Optimization For Architecting Hardware Accelerators. CoRR abs/2110.11346 (2021) - [i8]Leon Sixt, Evan Zheran Liu, Marie Pellat, James Wexler, Milad Hashemi, Been Kim, Martin Maas:
Analyzing a Caching Model. CoRR abs/2112.06989 (2021) - 2020
- [i7]Yujun Yan, Kevin Swersky, Danai Koutra, Parthasarathy Ranganathan, Milad Hashemi:
Neural Execution Engines: Learning to Execute Subroutines. CoRR abs/2006.08084 (2020) - [i6]Evan Zheran Liu, Milad Hashemi, Kevin Swersky, Parthasarathy Ranganathan, Junwhan Ahn:
An Imitation Learning Approach for Cache Replacement. CoRR abs/2006.16239 (2020) - [i5]Zhan Shi, Chirag Sakhuja, Milad Hashemi, Kevin Swersky, Calvin Lin:
Learned Hardware/Software Co-Design of Neural Accelerators. CoRR abs/2010.02075 (2020) - [i4]Will Grathwohl, Jacob Kelly, Milad Hashemi, Mohammad Norouzi, Kevin Swersky, David Duvenaud:
No MCMC for me: Amortized sampling for fast and stable training of energy-based models. CoRR abs/2010.04230 (2020) - 2019
- [i3]Zhan Shi, Kevin Swersky, Daniel Tarlow, Parthasarathy Ranganathan, Milad Hashemi:
Learning Execution through Neural Code Fusion. CoRR abs/1906.07181 (2019) - 2018
- [i2]Milad Hashemi, Kevin Swersky, Jamie A. Smith, Grant Ayers, Heiner Litz, Jichuan Chang, Christos Kozyrakis, Parthasarathy Ranganathan:
Learning Memory Access Patterns. CoRR abs/1803.02329 (2018) - 2016
- [i1]Milad Hashemi:
On-Chip Mechanisms to Reduce Effective Memory Access Latency. CoRR abs/1609.00306 (2016)
Coauthor Index
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