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Joel Emer

Joel Emer

MIT
United States

D-Index & Metrics

Computer Science

D-Index
72
Citations
33511
World Ranking
1638
National Ranking
846

Joel Emer publication distribution in Computer Science in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Computer Science in 2026. The highlighted bar marks where Joel Emer sits on this spectrum.

32–41 publications: 7 scientists 42–51 publications: 22 scientists 52–61 publications: 82 scientists 62–71 publications: 134 scientists 72–81 publications: 249 scientists 82–91 publications: 324 scientists 92–101 publications: 421 scientists 102–111 publications: 420 scientists 112–121 publications: 497 scientists 122–131 publications: 544 scientists 132–141 publications: 555 scientists 142–151 publications: 609 scientists 152–161 publications: 559 scientists 162–171 publications: 534 scientists 172–181 publications: 556 scientists 182–191 publications: 583 scientists 192–201 publications: 519 scientists 202–211 publications: 508 scientists 212–221 publications: 490 scientists 222–231 publications: 437 scientists 232–241 publications: 423 scientists 242–251 publications: 408 scientists 252–261 publications: 377 scientists 262–271 publications: 301 scientists 272–281 publications: 335 scientists 282–291 publications: 320 scientists 292–301 publications: 293 scientists 302–311 publications: 250 scientists 312–321 publications: 238 scientists 322–331 publications: 206 scientists 332–341 publications: 209 scientists 342–351 publications: 208 scientists 352–361 publications: 162 scientists 362–371 publications: 176 scientists 372–381 publications: 127 scientists 382–391 publications: 158 scientists 392–401 publications: 128 scientists 402–411 publications: 104 scientists 412–421 publications: 94 scientists 422–431 publications: 99 scientists 432–441 publications: 83 scientists 442–451 publications: 108 scientists 452–461 publications: 73 scientists 462–471 publications: 77 scientists 472–481 publications: 69 scientists 482–491 publications: 84 scientists 492–501 publications: 62 scientists 502–511 publications: 54 scientists 512–521 publications: 57 scientists 522–531 publications: 51 scientists 532–541 publications: 51 scientists 542–551 publications: 32 scientists 552–561 publications: 38 scientists 562–571 publications: 28 scientists 572–581 publications: 43 scientists 582–591 publications: 33 scientists 592–601 publications: 41 scientists 602–611 publications: 32 scientists 612–621 publications: 28 scientists 622–631 publications: 25 scientists 632–641 publications: 27 scientists 642–651 publications: 17 scientists 652–661 publications: 20 scientists 662–671 publications: 17 scientists 672–681 publications: 15 scientists 682–691 publications: 14 scientists 692–701 publications: 21 scientists 702–711 publications: 13 scientists 712–721 publications: 12 scientists 722–731 publications: 19 scientists 732–741 publications: 14 scientists 742–751 publications: 12 scientists 752–761 publications: 10 scientists 762–771 publications: 10 scientists 772–781 publications: 11 scientists 782–791 publications: 10 scientists 792–801 publications: 11 scientists 802–811 publications: 8 scientists 812–821 publications: 8 scientists 822–831 publications: 7 scientists 832–841 publications: 11 scientists 842–851 publications: 10 scientists 852–861 publications: 5 scientists 862–871 publications: 9 scientists 872–881 publications: 4 scientists 882–891 publications: 6 scientists 892–901 publications: 3 scientists 902–911 publications: 6 scientists 912–921 publications: 3 scientists 922–931 publications: 2 scientists 932–941 publications: 2 scientists 942–951 publications: 2 scientists 952–961 publications: 3 scientists 962–971 publications: 3 scientists 972–981 publications: 3 scientists 982–990 publications: 5 scientists 991+ publications: 100 scientists
32 publications 991+

This scientist: 215 publications — 52nd percentile

52% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 991 publications or more.

Joel Emer D-index placement in Computer Science in 2026

The chart shows the D-index (discipline H-index) distribution of Computer Science scientists ranked by Research.com in 2026. The highlighted bar marks where Joel Emer sits on this spectrum.

30–31 D-Index: 879 scientists 32–33 D-Index: 983 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 968 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 763 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 518 scientists 54–55 D-Index: 500 scientists 56–57 D-Index: 458 scientists 58–59 D-Index: 400 scientists 60–61 D-Index: 337 scientists 62–63 D-Index: 308 scientists 64–65 D-Index: 292 scientists 66–67 D-Index: 249 scientists 68–69 D-Index: 213 scientists 70–71 D-Index: 192 scientists 72–73 D-Index: 189 scientists 74–75 D-Index: 165 scientists 76–77 D-Index: 139 scientists 78–79 D-Index: 119 scientists 80–81 D-Index: 121 scientists 82–83 D-Index: 113 scientists 84–85 D-Index: 88 scientists 86–87 D-Index: 87 scientists 88–89 D-Index: 75 scientists 90–91 D-Index: 69 scientists 92–93 D-Index: 57 scientists 94–95 D-Index: 46 scientists 96–97 D-Index: 38 scientists 98–99 D-Index: 34 scientists 100–101 D-Index: 36 scientists 102–103 D-Index: 27 scientists 104–105 D-Index: 37 scientists 106–107 D-Index: 18 scientists 108–109 D-Index: 31 scientists 110–111 D-Index: 19 scientists 112–113 D-Index: 16 scientists 114–115 D-Index: 12 scientists 116–117 D-Index: 20 scientists 118–119 D-Index: 15 scientists 120–121 D-Index: 5 scientists 122–123 D-Index: 20 scientists 124–125 D-Index: 8 scientists 126–127 D-Index: 5 scientists 128–129 D-Index: 7 scientists 130 D-Index: 3 scientists 131+ D-Index: 98 scientists
30 D-Index 131+

This scientist: 72 D-Index — 89th percentile

89% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 131 D-Index or more.

Research.com Recognitions

  • 2020 - Member of the National Academy of Engineering For quantitative analysis of computer architecture and its application to architectural innovation in commercial microprocessors.
  • 2009 - ACM - IEEE CS Eckert-Mauchly Award For pioneering contributions to performance analysis and modeling methodologies; for design innovations in several significant industry microprocessors; and for deftly bridging research and development, academia and industry.
  • 2004 - ACM Fellow For contributions to computer architecture and performance analysis.
  • 2004 - IEEE Fellow For contributions to computer architecture and quantitative analysis of processor performance.

Overview

Joel Emer is affiliated with MIT in the United States, specializing in fields related to computer science and engineering. Their research portfolio heavily focuses on computer architecture, processor performance analysis, and neural computing technologies.

The scientist has contributed extensively to the literature in areas including:

  • Advanced Memory and Neural Computing
  • Ferroelectric and Negative Capacitance Devices
  • Parallel Computing and Optimization Techniques
  • Advanced Data Storage Technologies
  • Advanced Neural Network Applications
  • CCD and CMOS Imaging Sensors
  • Neural Networks and Reservoir Computing

Main fields of study covered by Joel Emer include:

  • Computer Science
  • Engineering

Subfields of study related to their work include:

  • Electrical and Electronic Engineering
  • Hardware and Architecture
  • Computer Networks and Communications
  • Artificial Intelligence
  • Computer Vision and Pattern Recognition

Joel Emer's recent published papers reflect a focus on both theoretical and applied aspects of computer systems:

  • "There's plenty of room at the Top: What will drive computer performance after Moore's law?", 2020, Science
  • "Efficient Processing of Deep Neural Networks", 2020, Synthesis lectures on computer architecture
  • "A 0.32-128 TOPS, Scalable Multi-Chip-Module-Based Deep Neural Network Inference Accelerator With Ground-Referenced Signaling in 16 nm", 2020, IEEE Journal of Solid-State Circuits
  • "How to Evaluate Deep Neural Network Processors: TOPS/W (Alone) Considered Harmful", 2020, IEEE Solid-State Circuits Magazine
  • "Simba", 2021, Communications of the ACM

Frequent co-authors collaborating with Joel Emer include:

  • Vivienne Sze
  • Yu-Hsin Chen
  • Tien-Ju Yang
  • Stephen W. Keckler
  • Michael Pellauer

The scientist frequently publishes in venues such as:

  • arXiv (Cornell University)
  • IEEE Journal of Solid-State Circuits
  • IEEE Design and Test
  • IEEE Micro
  • Science

Joel Emer has also contributed to academic books, notably one published by Morgan & Claypool Publishers:

  • Efficient Processing of Deep Neural Networks, 2020

The scientist has received multiple awards throughout their career, such as:

  • Member of the National Academy of Engineering (2020) for quantitative analysis of computer architecture and its application to architectural innovation in commercial microprocessors
  • ACM - IEEE CS Eckert-Mauchly Award (2009) for pioneering contributions to performance analysis, design innovations in industry microprocessors, and bridging research and development
  • IEEE Fellow (2004) for contributions to computer architecture and quantitative analysis of processor performance
  • ACM Fellow (2004) for contributions to computer architecture and performance analysis

Best Publications

  • Efficient Processing of Deep Neural Networks: A Tutorial and Survey

    Vivienne Sze;Yu-Hsin Chen;Tien-Ju Yang;Joel S. Emer

  • Eyeriss: An Energy-Efficient Reconfigurable Accelerator for Deep Convolutional Neural Networks

    Yu-Hsin Chen;Tushar Krishna;Joel S. Emer;Vivienne Sze

  • 14.5 Eyeriss: An energy-efficient reconfigurable accelerator for deep convolutional neural networks

    Yu-Hsin Chen;Tushar Krishna;Joel Emer;Vivienne Sze

  • Eyeriss: a spatial architecture for energy-efficient dataflow for convolutional neural networks

    Yu-Hsin Chen;Joel Emer;Vivienne Sze

  • Exploiting Choice: Instruction Fetch and Issue on an Implementable Simultaneous Multithreading Processor

    Dean M. Tullsen;Susan J. Eggers;Joel S. Emer;Henry M. Levy

  • A systematic methodology to compute the architectural vulnerability factors for a high-performance microprocessor

    Shubhendu S. Mukherjee;Christopher Weaver;Joel Emer;Steven K. Reinhardt

  • SCNN: An Accelerator for Compressed-sparse Convolutional Neural Networks

    Angshuman Parashar;Minsoo Rhu;Anurag Mukkara;Antonio Puglielli

  • Eyeriss v2: A Flexible Accelerator for Emerging Deep Neural Networks on Mobile Devices

    Yu-Hsin Chen;Tien-Ju Yang;Joel S. Emer;Vivienne Sze

  • Adaptive insertion policies for high performance caching

    Moinuddin K. Qureshi;Aamer Jaleel;Yale N. Patt;Simon C. Steely

  • High performance cache replacement using re-reference interval prediction (RRIP)

    Aamer Jaleel;Kevin B. Theobald;Simon C. Steely;Joel Emer

  • Simultaneous multithreading: a platform for next-generation processors

    S.J. Eggers;J.S. Emer;H.M. Leby;J.L. Lo

  • The soft error problem: an architectural perspective

    S.S. Mukherjee;J. Emer;S.K. Reinhardt

  • Timeloop: A Systematic Approach to DNN Accelerator Evaluation

    Angshuman Parashar;Priyanka Raina;Yakun Sophia Shao;Yu-Hsin Chen

  • Understanding error propagation in deep learning neural network (DNN) accelerators and applications

    Guanpeng Li;Siva Kumar Sastry Hari;Michael Sullivan;Timothy Tsai

  • Memory dependence prediction using store sets

    George Z. Chrysos;Joel S. Emer

  • There’s plenty of room at the Top: What will drive computer performance after Moore’s law?

    Charles E. Leiserson;Neil C. Thompson;Joel S. Emer;Joel S. Emer;Bradley C. Kuszmaul

  • Scheduling heterogeneous multi-cores through Performance Impact Estimation (PIE)

    Kenzo Van Craeynest;Aamer Jaleel;Lieven Eeckhout;Paolo Narvaez

  • Adaptive insertion policies for managing shared caches

    Aamer Jaleel;William Hasenplaugh;Moinuddin Qureshi;Julien Sebot

  • Converting thread-level parallelism to instruction-level parallelism via simultaneous multithreading

    Jack L. Lo;Joel S. Emer;Henry M. Levy;Rebecca L. Stamm

  • Techniques to Reduce the Soft Error Rate of a High-Performance Microprocessor

    Christopher Weaver;Joel Emer;Shubhendu S. Mukherjee;Steven K. Reinhardt

  • Eyeriss: A Spatial Architecture for Energy-Efficient Dataflow for Convolutional Neural Networks

    Yu-Hsin Chen;Joel S. Emer;Vivienne Sze

  • Eyeriss: A Spatial Architecture for Energy-Efficient Dataflow for Convolutional Neural Networks

    Yu-Hsin Chen;Joel Emer;Vivienne Sze

Frequent Co-Authors

Shubhendu S. Mukherjee
Shubhendu S. Mukherjee Cavium (United States)
Vivienne Sze
Vivienne Sze MIT
Aamer Jaleel
Aamer Jaleel Nvidia (United States)
Simon C. Steely
Simon C. Steely Intel (United States)
Stephen W. Keckler
Stephen W. Keckler Nvidia (United States)
Brucek Khailany
Brucek Khailany Nvidia (United States)
William J. Dally
William J. Dally Nvidia (United Kingdom)
Steven K. Reinhardt
Steven K. Reinhardt Advanced Micro Devices (United States)
Daniel Sanchez
Daniel Sanchez MIT
Yale N. Patt
Yale N. Patt The University of Texas at Austin

External Links

Wikipedia of Joel Emer Personal website of Joel Emer Google Scholar page

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Related Online Degrees & Career Pathways

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These diverse online degrees can complement a background in computer science, supporting a wide range of rewarding career pathways.

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