World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
39
Citations
8356
World Ranking
9610
National Ranking
4075

Brucek Khailany 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 Brucek Khailany 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: 105 publications — 10th percentile

10% of scientists in this discipline score the same or lower.

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

Brucek Khailany 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 Brucek Khailany 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: 39 D-Index — 33rd percentile

33% of scientists in this discipline score the same or lower.

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

Overview

Brucek Khailany is affiliated with Nvidia in the United States. Their research spans multiple domains within computer science and engineering, focusing extensively on areas such as VLSI and FPGA design techniques, parallel computing and optimization techniques, and advanced neural network applications.

Khailany's recent scholarly contributions include:

  • "Accelerating Chip Design With Machine Learning," 2020, IEEE Micro
  • "DREAMPlace: Deep Learning Toolkit-Enabled GPU Acceleration for Modern VLSI Placement," 2020, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
  • "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
  • "ABCDPlace: Accelerated Batch-Based Concurrent Detailed Placement on Multithreaded CPUs and GPUs," 2020, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
  • "A 17-95.6 TOPS/W Deep Learning Inference Accelerator with Per-Vector Scaled 4-bit Quantization for Transformers in 5nm," 2022, 2022 IEEE Symposium on VLSI Technology and Circuits (VLSI Technology and Circuits)

Their frequent co-authors are:

  • Haoxing Ren
  • Rangharajan Venkatesan
  • Nathaniel Pinckney
  • Brian Zimmer
  • William J. Dally

Khailany has published in a variety of venues, with notable frequent publication locations including:

  • arXiv (Cornell University)
  • Proceedings of the 59th ACM/IEEE Design Automation Conference
  • 2021 IEEE/ACM International Conference On Computer Aided Design (ICCAD)
  • IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
  • IEEE Journal of Solid-State Circuits

The scientist's work touches on the following main fields of study:

  • Computer Science
  • Engineering

Subfields of study associated with their research include:

  • Electrical and Electronic Engineering
  • Hardware and Architecture
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence
  • Industrial and Manufacturing Engineering

Main topics covered in their research are:

  • VLSI and FPGA Design Techniques
  • Parallel Computing and Optimization Techniques
  • Advanced Neural Network Applications
  • Advancements in Photolithography Techniques
  • VLSI and Analog Circuit Testing
  • Ferroelectric and Negative Capacitance Devices
  • Embedded Systems Design Techniques

Best Publications

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

    Angshuman Parashar;Minsoo Rhu;Anurag Mukkara;Antonio Puglielli

  • GPUs and the Future of Parallel Computing

    S. W. Keckler;W. J. Dally;B. Khailany;M. Garland

  • Imagine: media processing with streams

    B. Khailany;W.J. Dally;U.J. Kapasi;P. Mattson

  • Timeloop: A Systematic Approach to DNN Accelerator Evaluation

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

  • Programmable stream processors

    U.J. Kapasi;S. Rixner;W.J. Dally;B. Khailany

  • Register organization for media processing

    S. Rixner;W.J. Dally;B. Khailany;P. Mattson

  • The Imagine Stream Processor

    U.J. Kapasi;W.J. Dally;S. Rixner;J.D. Owens

  • Simba: Scaling Deep-Learning Inference with Multi-Chip-Module-Based Architecture

    Yakun Sophia Shao;Jason Clemons;Rangharajan Venkatesan;Brian Zimmer

  • A bandwidth-efficient architecture for media processing

    Scott Rixner;William J. Dally;Ujval J. Kapasi;Brucek Khailany

  • SCNN

    Unknown

  • CudaDMA: optimizing GPU memory bandwidth via warp specialization

    Michael Bauer;Henry Cook;Brucek Khailany

  • Evaluating the Imagine Stream Architecture

    Jung Ho Ahn;William J. Dally;Brucek Khailany;Ujval J. Kapasi

  • DREAMPlace: Deep Learning Toolkit-Enabled GPU Acceleration for Modern VLSI Placement

    Yibo Lin;Zixuan Jiang;Jiaqi Gu;Wuxi Li

  • Unifying Primary Cache, Scratch, and Register File Memories in a Throughput Processor

    Mark Gebhart;Stephen W. Keckler;Brucek Khailany;Ronny Krashinsky

  • Efficient conditional operations for data-parallel architectures

    Ujval J. Kapasi;William J. Dally;Scott Rixner;Peter R. Mattson

  • A Programmable 512 GOPS Stream Processor for Signal, Image, and Video Processing

    B.K. Khailany;T. Williams;J. Lin;E.P. Long

  • MAGNet: A Modular Accelerator Generator for Neural Networks

    Rangharajan Venkatesan;Priyanka Raina;Yanqing Zhang;Brian Zimmer

  • DREAMPIace: Deep Learning Toolkit-Enabled GPU Acceleration for Modern VLSI Placement

    Yibo Lin;Shounak Dhar;Wuxi Li;Haoxing Ren

  • Invited Paper: VerilogEval: Evaluating Large Language Models for Verilog Code Generation

    Unknown

  • A bandwidth-efficient architecture for media processing

    Unknown

  • Stream Processors: Progammability and Efficiency: Will this new kid on the block muscle out ASIC and DSP?

    William J. Dally;Ujval J. Kapasi;Brucek Khailany;Jung Ho Ahn

  • High Performance Graph ConvolutionaI Networks with Applications in Testability Analysis

    Yuzhe Ma;Haoxing Ren;Brucek Khailany;Harbinder Sikka

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

    Angshuman Parashar;Minsoo Rhu;Anurag Mukkara;Antonio Puglielli

  • A Programmable 512 GOPS Stream Processor for Signal, Image, and Video Processing

    B. Khailany;T. Williams;J. Lin;E. Long

Frequent Co-Authors

William J. Dally
William J. Dally Nvidia (United Kingdom)
Stephen W. Keckler
Stephen W. Keckler Nvidia (United States)
John D. Owens
John D. Owens University of California, Davis
Scott Rixner
Scott Rixner Rice University
David Z. Pan
David Z. Pan The University of Texas at Austin
Jung Ho Ahn
Jung Ho Ahn Seoul National University
Jiang Hu
Jiang Hu Texas A&M University
Sachin S. Sapatnekar
Sachin S. Sapatnekar University of Minnesota
Anand Raghunathan
Anand Raghunathan Purdue University West Lafayette

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