World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
38
Citations
12161
World Ranking
9965
National Ranking
4193

Tushar Krishna 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 Tushar Krishna 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: 160 publications — 31st percentile

31% of scientists in this discipline score the same or lower.

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

Tushar Krishna 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 Tushar Krishna 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: 38 D-Index — 30th percentile

30% of scientists in this discipline score the same or lower.

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

Overview

Tushar Krishna is affiliated with the Georgia Institute of Technology in the United States. Their work spans multiple aspects of computer science and engineering, with a strong emphasis on hardware architecture and deep learning accelerator design.

Their research includes publications in several key areas such as Parallel Computing and Optimization Techniques, Advanced Neural Network Applications, and Advanced Memory and Neural Computing. Other topics covered include Ferroelectric and Negative Capacitance Devices, Advanced Data Storage Technologies, Stochastic Gradient Optimization Techniques, and Embedded Systems Design Techniques.

Frequent coauthors collaborating with Tushar Krishna include Hyoukjun Kwon, Michael Pellauer, Angshuman Parashar, Ananda Samajdar, and Sheng-Chun Kao.

They have contributed to a variety of publication venues with several papers appearing in:

  • arXiv (Cornell University)
  • Zenodo (CERN European Organization for Nuclear Research)
  • IEEE Micro
  • IEEE Transactions on Very Large Scale Integration (VLSI) Systems
  • ACM Transactions on Architecture and Code Optimization

Notable recent papers authored or coauthored by Tushar Krishna include:

  • MAESTRO: A Data-Centric Approach to Understand Reuse, Performance, and Hardware Cost of DNN Mappings (2020, IEEE Micro)
  • Architecture, Chip, and Package Codesign Flow for Interposer-Based 2.5-D Chiplet Integration Enabling Heterogeneous IP Reuse (2020, IEEE Transactions on Very Large Scale Integration (VLSI) Systems)
  • Marvel: A Data-Centric Approach for Mapping Deep Learning Operators on Spatial Accelerators (2021, ACM Transactions on Architecture and Code Optimization)
  • Evaluating Spatial Accelerator Architectures with Tiled Matrix-Matrix Multiplication (2021, IEEE Transactions on Parallel and Distributed Systems)
  • The Fake-Busy and True-Idle Problems of Running Graph Applications on Chiplet-Based Multi-cores (2025, Zenodo (CERN European Organization for Nuclear Research))

Tushar Krishna has also authored a book titled Data Orchestration in Deep Learning Accelerators published in 2020 by Morgan & Claypool Publishers.

Their main fields of study are Computer Science and Engineering, with significant contributions to several subfields including Electrical and Electronic Engineering, Artificial Intelligence, Hardware and Architecture, Computer Vision and Pattern Recognition, and Computer Networks and Communications.

Best Publications

  • The gem5 simulator

    Nathan Binkert;Bradford Beckmann;Gabriel Black;Steven K. Reinhardt

  • GARNET: A detailed on-chip network model inside a full-system simulator

    Niket Agarwal;Tushar Krishna;Li-Shiuan Peh;Niraj K. Jha

  • SIGMA: A Sparse and Irregular GEMM Accelerator with Flexible Interconnects for DNN Training

    Eric Qin;Ananda Samajdar;Hyoukjun Kwon;Vineet Nadella

  • MAERI: Enabling Flexible Dataflow Mapping over DNN Accelerators via Reconfigurable Interconnects

    Hyoukjun Kwon;Ananda Samajdar;Tushar Krishna

  • Understanding Reuse, Performance, and Hardware Cost of DNN Dataflow: A Data-Centric Approach

    Hyoukjun Kwon;Prasanth Chatarasi;Michael Pellauer;Angshuman Parashar

  • SCALE-Sim: Systolic CNN Accelerator Simulator

    Ananda Samajdar;Yuhao Zhu;Paul Whatmough;Matthew Mattina

  • A Systematic Methodology for Characterizing Scalability of DNN Accelerators using SCALE-Sim

    Ananda Samajdar;Jan Moritz Joseph;Yuhao Zhu;Paul Whatmough

  • MAESTRO: A Data-Centric Approach to Understand Reuse, Performance, and Hardware Cost of DNN Mappings

    Hyoukjun Kwon;Prasanth Chatarasi;Vivek Sarkar;Tushar Krishna

  • SCORPIO: a 36-core research chip demonstrating snoopy coherence on a scalable mesh NoC with in-network ordering

    Bhavya K. Daya;Chia-Hsin Owen Chen;Suvinay Subramanian;Woo-Cheol Kwon

  • SMART: a single-cycle reconfigurable NoC for SoC applications

    Chia-Hsin Owen Chen;Sunghyun Park;Tushar Krishna;Suvinay Subramanian

  • Breaking the on-chip latency barrier using SMART

    Unknown

  • Approaching the theoretical limits of a mesh NoC with a 16-node chip prototype in 45nm SOI

    Sunghyun Park;Tushar Krishna;Chia-Hsin Chen;Bhavya Daya

  • Co-Exploration of Neural Architectures and Heterogeneous ASIC Accelerator Designs Targeting Multiple Tasks

    Lei Yang;Zheyu Yan;Meng Li;Hyoukjun Kwon

  • Towards the ideal on-chip fabric for 1-to-many and many-to-1 communication

    Tushar Krishna;Li-Shiuan Peh;Bradford M. Beckmann;Steven K. Reinhardt

  • ConfuciuX: Autonomous Hardware Resource Assignment for DNN Accelerators using Reinforcement Learning

    Sheng-Chun Kao;Geonhwa Jeong;Tushar Krishna

  • Characterizing the Deployment of Deep Neural Networks on Commercial Edge Devices

    Ramyad Hadidi;Jiashen Cao;Yilun Xie;Bahar Asgari

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

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

  • Heterogeneous Dataflow Accelerators for Multi-DNN Workloads

    Hyoukjun Kwon;Liangzhen Lai;Michael Pellauer;Tushar Krishna

  • GAMMA: automating the HW mapping of DNN models on accelerators via genetic algorithm

    Sheng-Chun Kao;Tushar Krishna

  • Architecture, Chip, and Package Codesign Flow for Interposer-Based 2.5-D Chiplet Integration Enabling Heterogeneous IP Reuse

    Jinwoo Kim;Gauthaman Murali;Heechun Park;Eric Qin

  • SCALE-Sim: Systolic CNN Accelerator.

    Ananda Samajdar;Yuhao Zhu;Paul N. Whatmough;Matthew Mattina

  • The gem5 Simulator: Version 20.0+

    Jason Lowe-Power;Abdul Ahmad;Adria Armejach;Adrian Herrera

Frequent Co-Authors

Sung Kyu Lim
Sung Kyu Lim Georgia Institute of Technology
Hyesoon Kim
Hyesoon Kim Georgia Institute of Technology
Vivek Sarkar
Vivek Sarkar Georgia Institute of Technology
Eduard Alarcon
Eduard Alarcon Universitat Politècnica de Catalunya
Natalie Enright Jerger
Natalie Enright Jerger University of Toronto
Rainer Leupers
Rainer Leupers RWTH Aachen University
Arijit Raychowdhury
Arijit Raychowdhury Georgia Institute of Technology
Madhavan Swaminathan
Madhavan Swaminathan Pennsylvania State University
Saibal Mukhopadhyay
Saibal Mukhopadhyay Georgia Institute of Technology
Xiaoli Ma
Xiaoli Ma Georgia Institute of Technology

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