Milad Hashemi

According to our database, Milad Hashemi authored at least 25 papers between 2012 and 2024.

Collaborative distances:

• Dijkstra number of three.
• Erdős number of four.

2024

Learning Performance-Improving Code Edits.

Alexander Shypula, Aman Madaan, Yimeng Zeng, Uri Alon, Jacob R. Gardner, Yiming Yang, Milad Hashemi, Graham Neubig, Parthasarathy Ranganathan, Osbert Bastani, Amir Yazdanbakhsh

Proceedings of the Twelfth International Conference on Learning Representations, 2024

Limoncello: Prefetchers for Scale.

Akanksha Jain, Hannah Lin, Carlos Villavieja, Baris Kasikci, Chris Kennelly, Milad Hashemi, Parthasarathy Ranganathan

Proceedings of the 29th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, 2024

2023

Towards Better Out-of-Distribution Generalization of Neural Algorithmic Reasoning Tasks.

Sadegh Mahdavi, Kevin Swersky, Thomas Kipf, Milad Hashemi, Christos Thrampoulidis, Renjie Liao

Trans. Mach. Learn. Res., 2023

Learning Performance-Improving Code Edits.

Aman Madaan, Alexander Shypula, Uri Alon, Milad Hashemi, Parthasarathy Ranganathan, Yiming Yang, Graham Neubig, Amir Yazdanbakhsh

CoRR, 2023

CUF: Continuous Upsampling Filters.

Cristina Nader Vasconcelos, A. Cengiz Öztireli, Mark J. Matthews, Milad Hashemi, Kevin Swersky, Andrea Tagliasacchi

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

2022

Learning to Improve Code Efficiency.

Binghong Chen, Daniel Tarlow, Kevin Swersky, Martin Maas, Pablo Ariel Heiber, Ashish Naik, Milad Hashemi, Parthasarathy Ranganathan

CoRR, 2022

Data-Driven Offline Optimization for Architecting Hardware Accelerators.

Aviral Kumar, Amir Yazdanbakhsh, Milad Hashemi, Kevin Swersky, Sergey Levine

Proceedings of the Tenth International Conference on Learning Representations, 2022

Two Sides of the Same Coin: Heterophily and Oversmoothing in Graph Convolutional Neural Networks.

Yujun Yan, Milad Hashemi, Kevin Swersky, Yaoqing Yang, Danai Koutra

Proceedings of the IEEE International Conference on Data Mining, 2022

2021

Analyzing a Caching Model.

Leon Sixt, Evan Zheran Liu, Marie Pellat, James Wexler, Milad Hashemi, Been Kim, Martin Maas

CoRR, 2021

Apollo: Transferable Architecture Exploration.

Amir Yazdanbakhsh, Christof Angermüller, Berkin Akin, Yanqi Zhou, Albin Jones, Milad Hashemi, Kevin Swersky, Satrajit Chatterjee, Ravi Narayanaswami, James Laudon

CoRR, 2021

Oops I Took A Gradient: Scalable Sampling for Discrete Distributions.

Will Grathwohl, Kevin Swersky, Milad Hashemi, David Duvenaud, Chris J. Maddison

Proceedings of the 38th International Conference on Machine Learning, 2021

No MCMC for me: Amortized sampling for fast and stable training of energy-based models.

Will Sussman Grathwohl, Jacob Jin Kelly, Milad Hashemi, Mohammad Norouzi, Kevin Swersky, David Duvenaud

Proceedings of the 9th International Conference on Learning Representations, 2021

A hierarchical neural model of data prefetching.

Zhan Shi, Akanksha Jain, Kevin Swersky, Milad Hashemi, Parthasarathy Ranganathan, Calvin Lin

Proceedings of the ASPLOS '21: 26th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, 2021

2020

Machine Learning for Systems.

Heiner Litz, Milad Hashemi

IEEE Micro, 2020

Learned Hardware/Software Co-Design of Neural Accelerators.

Zhan Shi, Chirag Sakhuja, Milad Hashemi, Kevin Swersky, Calvin Lin

CoRR, 2020

Neural Execution Engines: Learning to Execute Subroutines.

Yujun Yan, Kevin Swersky, Danai Koutra, Parthasarathy Ranganathan, Milad Hashemi

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

An Imitation Learning Approach for Cache Replacement.

Evan Zheran Liu, Milad Hashemi, Kevin Swersky, Parthasarathy Ranganathan, Junwhan Ahn

Proceedings of the 37th International Conference on Machine Learning, 2020

Learning Execution through Neural Code fusion.

Zhan Shi, Kevin Swersky, Daniel Tarlow, Parthasarathy Ranganathan, Milad Hashemi

Proceedings of the 8th International Conference on Learning Representations, 2020

2018

Learning Memory Access Patterns.

Milad Hashemi, Kevin Swersky, Jamie A. Smith, Grant Ayers, Heiner Litz, Jichuan Chang, Christos Kozyrakis, Parthasarathy Ranganathan

Proceedings of the 35th International Conference on Machine Learning, 2018

2016

On-Chip Mechanisms to Reduce Effective Memory Access Latency.

Milad Hashemi

CoRR, 2016

Efficient Execution of Bursty Applications.

Milad Hashemi, Debbie Marr, Doug Carmean, Yale N. Patt

IEEE Comput. Archit. Lett., 2016

Continuous runahead: Transparent hardware acceleration for memory intensive workloads.

Milad Hashemi, Onur Mutlu, Yale N. Patt

Proceedings of the 49th Annual IEEE/ACM International Symposium on Microarchitecture, 2016

Accelerating Dependent Cache Misses with an Enhanced Memory Controller.

Milad Hashemi, Khubaib, Eiman Ebrahimi, Onur Mutlu, Yale N. Patt

Proceedings of the 43rd ACM/IEEE Annual International Symposium on Computer Architecture, 2016

2015

Filtered runahead execution with a runahead buffer.

Milad Hashemi, Yale N. Patt

Proceedings of the 48th International Symposium on Microarchitecture, 2015

2012

MorphCore: An Energy-Efficient Microarchitecture for High Performance ILP and High Throughput TLP.

Khubaib, M. Aater Suleman, Milad Hashemi, Chris Wilkerson, Yale N. Patt

Proceedings of the 45th Annual IEEE/ACM International Symposium on Microarchitecture, 2012