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Computer Science

D-Index
37
Citations
6084
World Ranking
10726
National Ranking
4480

Overview

Mattan Erez is affiliated with The University of Texas at Austin in the United States. Their research primarily focuses on computer science, with specific contributions to artificial intelligence, hardware and architecture, information systems, computer networks and communications, and computer vision and pattern recognition.

Their work spans several key topics within computer science, including parallel computing and optimization techniques, advanced data storage technologies, cloud computing and resource management, stochastic gradient optimization techniques, advanced neural network applications, ferroelectric and negative capacitance devices, and computational physics and Python applications.

Frequent co-authors with whom Mattan Erez has collaborated are Benjamin Y. Cho, Jeageun Jung, Majid Jalili, Michael Orshansky, and Sangkug Lym.

The publication venues where Mattan Erez's research appears most often include:

  • arXiv (Cornell University)
  • IEEE Computer Architecture Letters
  • International Journal of Parallel Programming
  • 2021 36th IEEE/ACM International Conference on Automated Software Engineering (ASE)
  • Zenodo (CERN European Organization for Nuclear Research)

Notable recent papers authored or co-authored by Mattan Erez are:

  • Accelerating bandwidth-bound deep learning inference with main-memory accelerators, 2021, arXiv (Cornell University)
  • FlexSA: Flexible Systolic Array Architecture for Efficient Pruned DNN Model Training, 2020, arXiv (Cornell University)
  • Managing Prefetchers With Deep Reinforcement Learning, 2022, IEEE Computer Architecture Letters
  • Accelerating Bandwidth-Bound Deep Learning Inference with Main-Memory Accelerators, 2020, arXiv (Cornell University)
  • FlexSA: Flexible Systolic Array Architecture for Efficient Pruned DNN Model Training, 2020, arXiv (Cornell University)

Best Publications

  • Sequoia: programming the memory hierarchy

    Kayvon Fatahalian;Daniel Reiter Horn;Timothy J. Knight;Larkhoon Leem

  • Addressing failures in exascale computing

    Marc Snir;Robert W Wisniewski;Jacob A Abraham;Sarita V Adve

  • Merrimac: Supercomputing with Streams

    William J. Dally;Francois Labonte;Abhishek Das;Patrick Hanrahan

  • FREE-p: Protecting non-volatile memory against both hard and soft errors

    Doe Hyun Yoon;Naveen Muralimanohar;Jichuan Chang;Parthasarathy Ranganathan

  • Speculation techniques for improving load related instruction scheduling

    Adi Yoaz;Mattan Erez;Ronny Ronen;Stephan Jourdan

  • Balancing DRAM locality and parallelism in shared memory CMP systems

    Min Kyu Jeong;Doe Hyun Yoon;Dam Sunwoo;Mike Sullivan

  • Virtualized and flexible ECC for main memory

    Doe Hyun Yoon;Mattan Erez

  • A QoS-aware memory controller for dynamically balancing GPU and CPU bandwidth use in an MPSoC

    Min Kyu Jeong;Mattan Erez;Chander Sudanthi;Nigel Paver

  • Memory mapped ECC: low-cost error protection for last level caches

    Doe Hyun Yoon;Mattan Erez

  • A locality-aware memory hierarchy for energy-efficient GPU architectures

    Minsoo Rhu;Michael Sullivan;Jingwen Leng;Mattan Erez

  • Dirigent: Enforcing QoS for Latency-Critical Tasks on Shared Multicore Systems

    Haishan Zhu;Mattan Erez

  • NBTI-aware DVFS: a new approach to saving energy and increasing processor lifetime

    Mehmet Basoglu;Michael Orshansky;Mattan Erez

  • Adaptive granularity memory systems: a tradeoff between storage efficiency and throughput

    Doe Hyun Yoon;Min Kyu Jeong;Mattan Erez

  • Compilation for explicitly managed memory hierarchies

    Timothy J. Knight;Ji Young Park;Manman Ren;Mike Houston

  • Bamboo ECC: Strong, safe, and flexible codes for reliable computer memory

    Jungrae Kim;Michael Sullivan;Mattan Erez

  • Containment domains: a scalable, efficient, and flexible resilience scheme for exascale systems

    Jinsuk Chung;Ikhwan Lee;Michael Sullivan;Jee Ho Ryoo

  • Priority-based cache allocation in throughput processors

    Dong Li;Minsoo Rhu;Daniel R. Johnson;Mike O'Connor

  • NoC with Near-Ideal Express Virtual Channels Using Global-Line Communication

    T. Krishna;A. Kumar;P. Chiang;M. Erez

  • PruneTrain: fast neural network training by dynamic sparse model reconfiguration

    Sangkug Lym;Esha Choukse;Siavash Zangeneh;Wei Wen

  • The dynamic granularity memory system

    Doe Hyun Yoon;Min Kyu Jeong;Michael Sullivan;Mattan Erez

  • Bit-plane compression: transforming data for better compression in many-core architectures

    Jungrae Kim;Michael Sullivan;Esha Choukse;Mattan Erez

Frequent Co-Authors

William J. Dally
William J. Dally Nvidia (United Kingdom)
Jung Ho Ahn
Jung Ho Ahn Seoul National University
Michael Orshansky
Michael Orshansky The University of Texas at Austin
Stephen W. Keckler
Stephen W. Keckler Nvidia (United States)
Pat Hanrahan
Pat Hanrahan Stanford University
Alex Aiken
Alex Aiken Stanford University
Parthasarathy Ranganathan
Parthasarathy Ranganathan Google (United States)
Li-Shiuan Peh
Li-Shiuan Peh National University of Singapore
Avinoam Kolodny
Avinoam Kolodny Technion – Israel Institute of Technology
Ran Ginosar
Ran Ginosar Technion – Israel Institute of Technology

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