Milad Hashemi

38 papers A* 15Journal 22Unranked 1
YearRankTypeTitle / Venue / Authors
2025 J jnl
Automated High-Level Code Optimization for Warehouse Performance.
IEEE Micro
Alexander Shypula, Aman Madaan, Yimeng Zeng, Uri Alon, Jacob R. Gardner, Milad Hashemi, Graham Neubig, Parthasarathy Ranganathan, Osbert Bastani, Amir Yazdanbakhsh
2025 J jnl
ECO: An LLM-Driven Efficient Code Optimizer for Warehouse Scale Computers.
CoRR
Hannah Lin, Martin Maas, Maximilian Roquemore, Arman Hasanzadeh, Fred Lewis, Yusuf Simonson, Tzu-Wei Yang, Amir Yazdanbakhsh, Deniz Altinbüken, Florin Papa, Maggie Nolan Edmonds, Aditya Patil, Don Schwarz, Satish Chandra, Chris Kennelly, Milad Hashemi, Parthasarathy Ranganathan
2025 J jnl
SWE-fficiency: Can Language Models Optimize Real-World Repositories on Real Workloads?
CoRR
Jeffrey Jian Ma, Milad Hashemi, Amir Yazdanbakhsh, Kevin Swersky, Ofir Press, Enhui Li, Vijay Janapa Reddi, Parthasarathy Ranganathan
2024 A* conf
Learning Performance-Improving Code Edits.
ICLR
Alexander Shypula, Aman Madaan, Yimeng Zeng, Uri Alon, Jacob R. Gardner, Yiming Yang, Milad Hashemi, Graham Neubig, Parthasarathy Ranganathan, Osbert Bastani, Amir Yazdanbakhsh
2024 — conf
Limoncello: Prefetchers for Scale.
ASPLOS (3)
Akanksha Jain, Hannah Lin, Carlos Villavieja, Baris Kasikci, Chris Kennelly, Milad Hashemi, Parthasarathy Ranganathan
2023 A* conf
CUF: Continuous Upsampling Filters.
CVPR
Cristina Nader Vasconcelos, A. Cengiz Öztireli, Mark J. Matthews, Milad Hashemi, Kevin Swersky, Andrea Tagliasacchi
2023 J jnl
Learning Performance-Improving Code Edits.
CoRR
Aman Madaan, Alexander Shypula, Uri Alon, Milad Hashemi, Parthasarathy Ranganathan, Yiming Yang, Graham Neubig, Amir Yazdanbakhsh
2023 J jnl
Towards Better Out-of-Distribution Generalization of Neural Algorithmic Reasoning Tasks.
Trans. Mach. Learn. Res.
Sadegh Mahdavi, Kevin Swersky, Thomas Kipf, Milad Hashemi, Christos Thrampoulidis, Renjie Liao
2022 J jnl
CUF: Continuous Upsampling Filters.
CoRR
Cristina Nader Vasconcelos, Cengiz Öztireli, Mark J. Matthews, Milad Hashemi, Kevin Swersky, Andrea Tagliasacchi
2022 A* conf
Data-Driven Offline Optimization for Architecting Hardware Accelerators.
ICLR
Aviral Kumar, Amir Yazdanbakhsh, Milad Hashemi, Kevin Swersky, Sergey Levine
2022 J jnl
Learning to Improve Code Efficiency.
CoRR
Binghong Chen, Daniel Tarlow, Kevin Swersky, Martin Maas, Pablo Ariel Heiber, Ashish Naik, Milad Hashemi, Parthasarathy Ranganathan
2022 J jnl
Towards Better Out-of-Distribution Generalization of Neural Algorithmic Reasoning Tasks.
CoRR
Sadegh Mahdavi, Kevin Swersky, Thomas Kipf, Milad Hashemi, Christos Thrampoulidis, Renjie Liao
2022 A* conf
Two Sides of the Same Coin: Heterophily and Oversmoothing in Graph Convolutional Neural Networks.
ICDM
Yujun Yan, Milad Hashemi, Kevin Swersky, Yaoqing Yang, Danai Koutra
2021 A* conf
A hierarchical neural model of data prefetching.
ASPLOS
Zhan Shi, Akanksha Jain, Kevin Swersky, Milad Hashemi, Parthasarathy Ranganathan, Calvin Lin
2021 J jnl
Analyzing a Caching Model.
CoRR
Leon Sixt, Evan Zheran Liu, Marie Pellat, James Wexler, Milad Hashemi, Been Kim, Martin Maas
2021 J jnl
Apollo: Transferable Architecture Exploration.
CoRR
Amir Yazdanbakhsh, Christof Angermüller, Berkin Akin, Yanqi Zhou, Albin Jones, Milad Hashemi, Kevin Swersky, Satrajit Chatterjee, Ravi Narayanaswami, James Laudon
2021 J jnl
Data-Driven Offline Optimization For Architecting Hardware Accelerators.
CoRR
Aviral Kumar, Amir Yazdanbakhsh, Milad Hashemi, Kevin Swersky, Sergey Levine
2021 A* conf
No MCMC for me: Amortized sampling for fast and stable training of energy-based models.
ICLR
Will Sussman Grathwohl, Jacob Jin Kelly, Milad Hashemi, Mohammad Norouzi, Kevin Swersky, David Duvenaud
2021 A* conf
Oops I Took A Gradient: Scalable Sampling for Discrete Distributions.
ICML
Will Grathwohl, Kevin Swersky, Milad Hashemi, David Duvenaud, Chris J. Maddison
2021 J jnl
Oops I Took A Gradient: Scalable Sampling for Discrete Distributions.
CoRR
Will Grathwohl, Kevin Swersky, Milad Hashemi, David Duvenaud, Chris J. Maddison
2021 J jnl
Two Sides of the Same Coin: Heterophily and Oversmoothing in Graph Convolutional Neural Networks.
CoRR
Yujun Yan, Milad Hashemi, Kevin Swersky, Yaoqing Yang, Danai Koutra
2020 A* conf
An Imitation Learning Approach for Cache Replacement.
ICML
Evan Zheran Liu, Milad Hashemi, Kevin Swersky, Parthasarathy Ranganathan, Junwhan Ahn
2020 J jnl
An Imitation Learning Approach for Cache Replacement.
CoRR
Evan Zheran Liu, Milad Hashemi, Kevin Swersky, Parthasarathy Ranganathan, Junwhan Ahn
2020 J jnl
Learned Hardware/Software Co-Design of Neural Accelerators.
CoRR
Zhan Shi, Chirag Sakhuja, Milad Hashemi, Kevin Swersky, Calvin Lin
2020 A* conf
Learning Execution through Neural Code fusion.
ICLR
Zhan Shi, Kevin Swersky, Daniel Tarlow, Parthasarathy Ranganathan, Milad Hashemi
2020 J jnl
Machine Learning for Systems.
IEEE Micro
Heiner Litz, Milad Hashemi
2020 A* conf
Neural Execution Engines: Learning to Execute Subroutines.
NeurIPS
Yujun Yan, Kevin Swersky, Danai Koutra, Parthasarathy Ranganathan, Milad Hashemi
2020 J jnl
Neural Execution Engines: Learning to Execute Subroutines.
CoRR
Yujun Yan, Kevin Swersky, Danai Koutra, Parthasarathy Ranganathan, Milad Hashemi
2020 J jnl
No MCMC for me: Amortized sampling for fast and stable training of energy-based models.
CoRR
Will Grathwohl, Jacob Kelly, Milad Hashemi, Mohammad Norouzi, Kevin Swersky, David Duvenaud
2019 J jnl
Learning Execution through Neural Code Fusion.
CoRR
Zhan Shi, Kevin Swersky, Daniel Tarlow, Parthasarathy Ranganathan, Milad Hashemi
2018 A* conf
Learning Memory Access Patterns.
ICML
Milad Hashemi, Kevin Swersky, Jamie A. Smith, Grant Ayers, Heiner Litz, Jichuan Chang, Christos Kozyrakis, Parthasarathy Ranganathan
2018 J jnl
Learning Memory Access Patterns.
CoRR
Milad Hashemi, Kevin Swersky, Jamie A. Smith, Grant Ayers, Heiner Litz, Jichuan Chang, Christos Kozyrakis, Parthasarathy Ranganathan
2016 A* conf
Accelerating Dependent Cache Misses with an Enhanced Memory Controller.
ISCA
Milad Hashemi, Khubaib, Eiman Ebrahimi, Onur Mutlu, Yale N. Patt
2016 A* conf
Continuous runahead: Transparent hardware acceleration for memory intensive workloads.
MICRO
Milad Hashemi, Onur Mutlu, Yale N. Patt
2016 J jnl
Efficient Execution of Bursty Applications.
IEEE Comput. Archit. Lett.
Milad Hashemi, Debbie Marr, Doug Carmean, Yale N. Patt
2016 J jnl
On-Chip Mechanisms to Reduce Effective Memory Access Latency.
CoRR
Milad Hashemi
2015 A* conf
Filtered runahead execution with a runahead buffer.
MICRO
Milad Hashemi, Yale N. Patt
2012 A* conf
MorphCore: An Energy-Efficient Microarchitecture for High Performance ILP and High Throughput TLP.
MICRO
Khubaib, M. Aater Suleman, Milad Hashemi, Chris Wilkerson, Yale N. Patt