World's Best Scientists 2026 revealed!

D-Index & Metrics

Engineering and Technology

D-Index
37
Citations
11401
World Ranking
8239
National Ranking
2268

Michael Taylor publication distribution in Engineering and Technology in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Engineering and Technology in 2026. The highlighted bar marks where Michael Taylor sits on this spectrum.

38–47 publications: 20 scientists 48–57 publications: 35 scientists 58–67 publications: 96 scientists 68–77 publications: 135 scientists 78–87 publications: 190 scientists 88–97 publications: 259 scientists 98–107 publications: 283 scientists 108–117 publications: 369 scientists 118–127 publications: 341 scientists 128–137 publications: 386 scientists 138–147 publications: 372 scientists 148–157 publications: 457 scientists 158–167 publications: 415 scientists 168–177 publications: 407 scientists 178–187 publications: 421 scientists 188–197 publications: 378 scientists 198–207 publications: 403 scientists 208–217 publications: 317 scientists 218–227 publications: 346 scientists 228–237 publications: 321 scientists 238–247 publications: 260 scientists 248–257 publications: 280 scientists 258–267 publications: 240 scientists 268–277 publications: 214 scientists 278–287 publications: 242 scientists 288–297 publications: 203 scientists 298–307 publications: 166 scientists 308–317 publications: 154 scientists 318–327 publications: 175 scientists 328–337 publications: 159 scientists 338–347 publications: 99 scientists 348–357 publications: 131 scientists 358–367 publications: 106 scientists 368–377 publications: 118 scientists 378–387 publications: 97 scientists 388–397 publications: 108 scientists 398–407 publications: 82 scientists 408–417 publications: 71 scientists 418–427 publications: 64 scientists 428–437 publications: 55 scientists 438–447 publications: 54 scientists 448–457 publications: 60 scientists 458–467 publications: 47 scientists 468–477 publications: 40 scientists 478–487 publications: 30 scientists 488–497 publications: 29 scientists 498–507 publications: 38 scientists 508–517 publications: 40 scientists 518–527 publications: 32 scientists 528–537 publications: 23 scientists 538–547 publications: 28 scientists 548–557 publications: 23 scientists 558–567 publications: 19 scientists 568–577 publications: 16 scientists 578–587 publications: 17 scientists 588–597 publications: 18 scientists 598–607 publications: 22 scientists 608–617 publications: 15 scientists 618–627 publications: 9 scientists 628–637 publications: 11 scientists 638–647 publications: 21 scientists 648–657 publications: 12 scientists 658–667 publications: 9 scientists 668–677 publications: 11 scientists 678–687 publications: 9 scientists 688–697 publications: 6 scientists 698–707 publications: 14 scientists 708–717 publications: 7 scientists 718–727 publications: 8 scientists 728–737 publications: 10 scientists 738–747 publications: 9 scientists 748–757 publications: 5 scientists 758–767 publications: 5 scientists 768–777 publications: 11 scientists 778–787 publications: 7 scientists 788–797 publications: 2 scientists 798–803 publications: 4 scientists 804+ publications: 100 scientists
38 publications 804+

This scientist: 118 publications — 14th percentile

14% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 804 publications or more.

Michael Taylor D-index placement in Engineering and Technology in 2026

The chart shows the D-index (discipline H-index) distribution of Engineering and Technology scientists ranked by Research.com in 2026. The highlighted bar marks where Michael Taylor sits on this spectrum.

30 D-Index: 59 scientists 31 D-Index: 114 scientists 32 D-Index: 129 scientists 33 D-Index: 189 scientists 34 D-Index: 200 scientists 35 D-Index: 262 scientists 36 D-Index: 311 scientists 37 D-Index: 312 scientists 38 D-Index: 350 scientists 39 D-Index: 385 scientists 40 D-Index: 348 scientists 41 D-Index: 362 scientists 42 D-Index: 426 scientists 43 D-Index: 380 scientists 44 D-Index: 310 scientists 45 D-Index: 341 scientists 46 D-Index: 301 scientists 47 D-Index: 306 scientists 48 D-Index: 271 scientists 49 D-Index: 246 scientists 50 D-Index: 210 scientists 51 D-Index: 253 scientists 52 D-Index: 213 scientists 53 D-Index: 221 scientists 54 D-Index: 195 scientists 55 D-Index: 186 scientists 56 D-Index: 170 scientists 57 D-Index: 167 scientists 58 D-Index: 166 scientists 59 D-Index: 144 scientists 60 D-Index: 152 scientists 61 D-Index: 141 scientists 62 D-Index: 138 scientists 63 D-Index: 131 scientists 64 D-Index: 118 scientists 65 D-Index: 114 scientists 66 D-Index: 119 scientists 67 D-Index: 95 scientists 68 D-Index: 87 scientists 69 D-Index: 77 scientists 70 D-Index: 89 scientists 71 D-Index: 69 scientists 72 D-Index: 54 scientists 73 D-Index: 46 scientists 74 D-Index: 55 scientists 75 D-Index: 54 scientists 76 D-Index: 49 scientists 77 D-Index: 53 scientists 78 D-Index: 46 scientists 79 D-Index: 28 scientists 80 D-Index: 39 scientists 81 D-Index: 36 scientists 82 D-Index: 24 scientists 83 D-Index: 26 scientists 84 D-Index: 36 scientists 85 D-Index: 18 scientists 86 D-Index: 25 scientists 87 D-Index: 19 scientists 88 D-Index: 26 scientists 89 D-Index: 27 scientists 90 D-Index: 23 scientists 91 D-Index: 15 scientists 92 D-Index: 12 scientists 93 D-Index: 9 scientists 94 D-Index: 15 scientists 95 D-Index: 10 scientists 96 D-Index: 13 scientists 97 D-Index: 13 scientists 98 D-Index: 9 scientists 99 D-Index: 7 scientists 100 D-Index: 7 scientists 101 D-Index: 8 scientists 102 D-Index: 7 scientists 103 D-Index: 7 scientists 104 D-Index: 9 scientists 105 D-Index: 6 scientists 106 D-Index: 9 scientists 107+ D-Index: 99 scientists
30 D-Index 107+

This scientist: 37 D-Index — 16th percentile

16% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 107 D-Index or more.

Overview

Michael Taylor is affiliated with the University of Washington in the United States. Their research spans across several fields, primarily including Computer Science and Engineering. Within these, Taylor's work focuses on subfields such as Hardware and Architecture, Computer Networks and Communications, Electrical and Electronic Engineering, Artificial Intelligence, and Computational Mathematics.

The scientist's research topics include:

  • Parallel Computing and Optimization Techniques
  • Interconnection Networks and Systems
  • Advanced Data Storage Technologies
  • Radiation Effects in Electronics
  • Security and Verification in Computing
  • Low-power high-performance VLSI design
  • Tensor decomposition and applications

Michael Taylor has published in a variety of notable venues, such as:

  • IEEE Micro
  • IEEE Journal of Solid-State Circuits
  • IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
  • Communications of the ACM
  • arXiv (Cornell University)

Their recent papers include:

  • "BlackParrot: An Agile Open-Source RISC-V Multicore for Accelerator SoCs," published in 2020 in IEEE Micro
  • "A 7.3 M Output Non-Zeros/J, 11.7 M Output Non-Zeros/GB Reconfigurable Sparse Matrix-Matrix Multiplication Accelerator," published in 2020 in IEEE Journal of Solid-State Circuits
  • "ASIC clouds," published in 2020 in Communications of the ACM
  • "A Tensor Processing Framework for CPU-Manycore Heterogeneous Systems," published in 2021 in IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
  • "Chiplet Cloud: Building AI Supercomputers for Serving Large Generative Language Models," published in 2023 in arXiv (Cornell University)

Frequent collaborators in Michael Taylor's research include:

  • Scott Davidson
  • Dai Cheol Jung
  • Paul Gao
  • Chun Zhao
  • Bandhav Veluri

Best Publications

  • Autonomous driving in urban environments: Boss and the Urban Challenge

    Chris Urmson;Joshua Anhalt;Drew Bagnell;Christopher Baker

  • The Raw microprocessor: a computational fabric for software circuits and general-purpose programs

    M.B. Taylor;J. Kim;J. Miller;D. Wentzlaff

  • Baring it all to software: Raw machines

    E. Waingold;M. Taylor;D. Srikrishna;V. Sarkar

  • Evaluation of the Raw Microprocessor: An Exposed-Wire-Delay Architecture for ILP and Streams

    Michael Bedford Taylor;Walter Lee;Jason Miller;David Wentzlaff

  • Conservation cores: reducing the energy of mature computations

    Ganesh Venkatesh;Jack Sampson;Nathan Goulding;Saturnino Garcia

  • Is dark silicon useful?: harnessing the four horsemen of the coming dark silicon apocalypse

    Michael B. Taylor

  • Effects of central nervous system antiretroviral penetration on cognitive functioning in the ALLRT cohort.

    Marlene Smurzynski;Kunling Wu;Scott Letendre;Kevin Robertson

  • Scalar operand networks: on-chip interconnect for ILP in partitioned architectures

    M. Bedford Taylor;W. Lee;S. Amarasinghe;A. Agarwal

  • A landscape of the new dark silicon design regime

    Michael B. Taylor

  • SD-VBS: The San Diego Vision Benchmark Suite

    Sravanthi Kota Venkata;Ikkjin Ahn;Donghwan Jeon;Anshuman Gupta

  • Energy characterization of a tiled architecture processor with on-chip networks

    Jason Sungtae Kim;Michael Bedford Taylor;Jason Miller;David Wentzlaff

  • The GreenDroid Mobile Application Processor: An Architecture for Silicon's Dark Future

    N Goulding-Hotta;J Sampson;G Venkatesh;S Garcia

  • The Evolution of Bitcoin Hardware

    Michael Bedford Taylor

  • Bitcoin and the age of bespoke silicon

    Michael Bedford Taylor

  • QsCores: trading dark silicon for scalable energy efficiency with quasi-specific cores

    Ganesh Venkatesh;Jack Sampson;Nathan Goulding-Hotta;Sravanthi Kota Venkata

  • Scalar operand networks

    M.D. Taylor;W. Lee;S.P. Amarasinghe;A. Agarwal

  • Quantification of accuracy and precision of multi-center DTI measurements: A diffusion phantom and human brain study ☆

    Tong Zhu;Rui Hu;Xing Qiu;Michael Taylor

  • A 16-issue multiple-program-counter microprocessor with point-to-point scalar operand network

    M.B. Taylor;J. Kim;J. Miller;D. Wentzlaff

  • Kremlin: rethinking and rebooting gprof for the multicore age

    Saturnino Garcia;Donghwan Jeon;Christopher M. Louie;Michael Bedford Taylor

  • The RAW benchmark suite: computation structures for general purpose computing

    J. Babb;M. Frank;V. Lee;E. Waingold

Frequent Co-Authors

Graeme B. Segal
Graeme B. Segal University of Oxford
Steven Swanson
Steven Swanson University of California, San Diego
Ronald A. Cohen
Ronald A. Cohen University of Florida
Elyse J. Singer
Elyse J. Singer University of California, Los Angeles
Eric S. Daar
Eric S. Daar Lundquist Institute
Ronald G. Dreslinski
Ronald G. Dreslinski University of Michigan–Ann Arbor
Scott Letendre
Scott Letendre University of California, San Diego
Constantin T. Yiannoutsos
Constantin T. Yiannoutsos Indiana University – Purdue University Indianapolis

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