MorphCore: An Energy-Efficient Microarchitecture for High Performance ILP and High Throughput TLP. - Khubaib; M. Aater Suleman; Milad Hashemi; Chris Wilkerson; Yale N. Patt (MICRO, 2012)

Filtered runahead execution with a runahead buffer. - Milad Hashemi; Yale N. Patt (MICRO, 2015)

Accelerating Dependent Cache Misses with an Enhanced Memory Controller. - Milad Hashemi; Khubaib; Eiman Ebrahimi; Onur Mutlu; Yale N. Patt (ISCA, 2016)

Continuous runahead: Transparent hardware acceleration for memory intensive workloads. - Milad Hashemi; Onur Mutlu; Yale N. Patt (MICRO, 2016)

Efficient Execution of Bursty Applications. - Milad Hashemi; Debbie Marr; Doug Carmean; Yale N. Patt (IEEE Comput. Archit. Lett., 2016)

On-Chip Mechanisms to Reduce Effective Memory Access Latency. - Milad Hashemi (CoRR, 2016)

Learning Memory Access Patterns. - Milad Hashemi; Kevin Swersky; Jamie A. Smith; Grant Ayers; Heiner Litz; Jichuan Chang; Christos Kozyrakis; Parthasarathy Ranganathan (ICML, 2018)

Learning Execution through Neural Code fusion. - Zhan Shi; Kevin Swersky; Daniel Tarlow; Parthasarathy Ranganathan; Milad Hashemi (ICLR, 2020)

An Imitation Learning Approach for Cache Replacement. - Evan Zheran Liu; Milad Hashemi; Kevin Swersky; Parthasarathy Ranganathan; Junwhan Ahn (ICML, 2020)

Neural Execution Engines: Learning to Execute Subroutines. - Yujun Yan; Kevin Swersky; Danai Koutra; Parthasarathy Ranganathan; Milad Hashemi (NeurIPS, 2020)

Learned Hardware/Software Co-Design of Neural Accelerators. - Zhan Shi; Chirag Sakhuja; Milad Hashemi; Kevin Swersky; Calvin Lin (CoRR, 2020)

Machine Learning for Systems. - Heiner Litz; Milad Hashemi (IEEE Micro, 2020)

A hierarchical neural model of data prefetching. - Zhan Shi; Akanksha Jain; Kevin Swersky; Milad Hashemi; Parthasarathy Ranganathan; Calvin Lin (ASPLOS, 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 002; Kevin Swersky; David Duvenaud (ICLR, 2021)

Oops I Took A Gradient: Scalable Sampling for Discrete Distributions. - Will Grathwohl; Kevin Swersky; Milad Hashemi; David Duvenaud; Chris J. Maddison (ICML, 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)

Analyzing a Caching Model. - Leon Sixt; Evan Zheran Liu; Marie Pellat; James Wexler; Milad Hashemi; Been Kim; Martin Maas 001 (CoRR, 2021)

Two Sides of the Same Coin: Heterophily and Oversmoothing in Graph Convolutional Neural Networks. - Yujun Yan; Milad Hashemi; Kevin Swersky; Yaoqing Yang; Danai Koutra (ICDM, 2022)

Data-Driven Offline Optimization for Architecting Hardware Accelerators. - Aviral Kumar; Amir Yazdanbakhsh; Milad Hashemi; Kevin Swersky; Sergey Levine (ICLR, 2022)

Learning to Improve Code Efficiency. - Binghong Chen; Daniel Tarlow; Kevin Swersky; Martin Maas 001; Pablo Ariel Heiber; Ashish Naik; Milad Hashemi; Parthasarathy Ranganathan (CoRR, 2022)

CUF: Continuous Upsampling Filters. - Cristina Nader Vasconcelos; A. Cengiz Öztireli; Mark J. Matthews; Milad Hashemi; Kevin Swersky; Andrea Tagliasacchi (CVPR, 2023)

Learning Performance-Improving Code Edits. - Aman Madaan; Alexander Shypula; Uri Alon 002; Milad Hashemi; Parthasarathy Ranganathan; Yiming Yang; Graham Neubig; Amir Yazdanbakhsh (CoRR, 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)

Limoncello: Prefetchers for Scale. - Akanksha Jain; Hannah Lin; Carlos Villavieja; Baris Kasikci; Chris Kennelly; Milad Hashemi; Parthasarathy Ranganathan (ASPLOS (3), 2024)

Learning Performance-Improving Code Edits. - Alexander Shypula; Aman Madaan; Yimeng Zeng; Uri Alon 002; Jacob R. Gardner; Yiming Yang; Milad Hashemi; Graham Neubig; Parthasarathy Ranganathan; Osbert Bastani; Amir Yazdanbakhsh (ICLR, 2024)

ECO: An LLM-Driven Efficient Code Optimizer for Warehouse Scale Computers. - Hannah Lin; Martin Maas 001; Maximilian Roquemore; Arman Hasanzadeh; Fred Lewis; Yusuf Simonson; Tzu-Wei Yang; Amir Yazdanbakhsh; Deniz Altinbüken; Florin Papa; Maggie Nolan Edmonds; Aditya Patil; et al. (CoRR, 2025)