World's Best Scientists 2026 revealed!
Vivienne Sze

Vivienne Sze

D-Index & Metrics

Computer Science

D-Index
49
Citations
21293
World Ranking
5747
National Ranking
2612

Vivienne Sze 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 Vivienne Sze 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: 151 publications — 27th percentile

27% of scientists in this discipline score the same or lower.

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

Vivienne Sze 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 Vivienne Sze 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: 49 D-Index — 60th percentile

60% of scientists in this discipline score the same or lower.

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

Overview

Vivienne Sze is a researcher affiliated with MIT in the United States. Their academic work spans the fields of Computer Science and Engineering, with a focused involvement in subfields such as Computer Vision and Pattern Recognition, Electrical and Electronic Engineering, Aerospace Engineering, Artificial Intelligence, and Computer Networks and Communications.

The main topics covered by Vivienne Sze's research include Robotics and Sensor-Based Localization, Advanced Vision and Imaging, Advanced Neural Network Applications, Advanced Memory and Neural Computing, Ferroelectric and Negative Capacitance Devices, Robotic Path Planning Algorithms, and Gaze Tracking and Assistive Technology.

Some of the recent papers authored or co-authored by Vivienne Sze include:

  • Efficient Processing of Deep Neural Networks, 2020, Synthesis Lectures on Computer Architecture
  • How to Evaluate Deep Neural Network Processors: TOPS/W (Alone) Considered Harmful, 2020, IEEE Solid-State Circuits Magazine
  • Data Centers on Wheels: Emissions From Computing Onboard Autonomous Vehicles, 2022, IEEE Micro
  • FSMI: Fast computation of Shannon mutual information for information-theoretic mapping, 2020, The International Journal of Robotics Research
  • Searching for Efficient Multi-Stage Vision Transformers, 2021, arXiv (Cornell University)

Vivienne Sze has collaborated frequently with several co-authors, including Joel Emer, Tien-Ju Yang, Yu-Hsin Chen, Sertaç Karaman, and Peter Zhi Xuan Li. These collaborations have contributed to a variety of publications and research outputs over time.

Their work has been published in multiple venues, with notable appearances in arXiv (Cornell University), IEEE Solid-State Circuits Magazine, the 2022 International Conference on Robotics and Automation (ICRA), IEEE Robotics and Automation Letters, and Synthesis Lectures on Computer Architecture.

In addition to journal articles and conference papers, Vivienne Sze is credited with book publications through Morgan & Claypool Publishers, which includes the title Efficient Processing of Deep Neural Networks published in 2020.

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

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

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

  • Designing Energy-Efficient Convolutional Neural Networks Using Energy-Aware Pruning

    Tien-Ju Yang;Yu-Hsin Chen;Vivienne Sze

  • High Efficiency Video Coding (HEVC)

    Vivienne Sze;Madhukar Budagavi;Gary J. Sullivan

  • NetAdapt: Platform-Aware Neural Network Adaptation for Mobile Applications

    Tien-Ju Yang;Andrew G. Howard;Bo Chen;Xiao Zhang

  • High Throughput CABAC Entropy Coding in HEVC

    V. Sze;M. Budagavi

  • Hardware for machine learning: Challenges and opportunities

    Vivienne Sze;Yu-Hsin Chen;Joel Einer;Amr Suleiman

  • FastDepth: Fast Monocular Depth Estimation on Embedded Systems

    Diana Wofk;Fangchang Ma;Tien-Ju Yang;Sertac Karaman

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

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

  • High Efficiency Video Coding (HEVC): Algorithms and Architectures

    Vivienne Sze;Madhukar Budagavi;Gary J. Sullivan

  • DeeperLab: Single-Shot Image Parser

    Tien-Ju Yang;Maxwell D. Collins;Yukun Zhu;Jyh-Jing Hwang

  • Core Transform Design in the High Efficiency Video Coding (HEVC) Standard

    Madhukar Budagavi;Arild Fuldseth;Gisle Bjontegaard;Vivienne Sze

  • A method to estimate the energy consumption of deep neural networks

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

  • Accelergy: An Architecture-Level Energy Estimation Methodology for Accelerator Designs

    Yannan Nellie Wu;Joel S. Emer;Vivienne Sze

  • Efficient Processing of Deep Neural Networks

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

  • Using Dataflow to Optimize Energy Efficiency of Deep Neural Network Accelerators

    Yu-Hsin Chen;Joel Emer;Vivienne Sze

  • Navion: A 2-mW Fully Integrated Real-Time Visual-Inertial Odometry Accelerator for Autonomous Navigation of Nano Drones

    Amr Suleiman;Zhengdong Zhang;Luca Carlone;Sertac Karaman

  • Hardware for Machine Learning: Challenges and Opportunities

    Vivienne Sze;Yu-Hsin Chen;Joel Emer;Amr Suleiman

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

    Yu-Hsin Chen;Joel Emer;Vivienne Sze

Frequent Co-Authors

Gary J. Sullivan
Gary J. Sullivan Microsoft (United States)
Naveen Verma
Naveen Verma Princeton University
Patrick P. Mercier
Patrick P. Mercier University of California, San Diego
David D. Wentzloff
David D. Wentzloff University of Michigan–Ann Arbor

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