World's Best Scientists 2026 revealed!
Swagath Venkataramani

Swagath Venkataramani

Award Badge
Rising Stars
2025

D-Index & Metrics

Rising Stars

D-Index
32
Citations
5180
World Ranking
965
National Ranking
157

Computer Science

D-Index
32
Citations
5124
World Ranking
13060
National Ranking
5258

Swagath Venkataramani 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 Swagath Venkataramani 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: 97 publications — 8th percentile

8% of scientists in this discipline score the same or lower.

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

Swagath Venkataramani 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 Swagath Venkataramani 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: 32 D-Index — 10th percentile

10% 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

  • 2025 - Research.com Rising Stars Award

Overview

Swagath Venkataramani is affiliated with IBM in the United States and has contributed extensively to research in computer science, with a focus on artificial intelligence and related fields. Their work spans multiple subfields including artificial intelligence, computer vision and pattern recognition, electrical and electronic engineering, hardware and architecture, and information systems and management.

The scientist has published a total of 33 works predominantly in areas connected to artificial intelligence and neural network applications as well as optimization techniques for parallel computing. Their main research topics highlight advanced neural network applications, parallel computing and optimization techniques, domain adaptation and few-shot learning, scientific computing and data management, ferroelectric and negative capacitance devices, advanced memory and neural computing, and COVID-19 diagnosis using AI.

Swagath Venkataramani's recent papers include the following:

  • Efficient AI System Design With Cross-Layer Approximate Computing (2020), Proceedings of the IEEE
  • ScaleCom: Scalable Sparsified Gradient Compression for Communication-Efficient Distributed Training (2021), arXiv (Cornell University)
  • A 7-nm Four-Core Mixed-Precision AI Chip With 26.2-TFLOPS Hybrid-FP8 Training, 104.9-TOPS INT4 Inference, and Workload-Aware Throttling (2021), IEEE Journal of Solid-State Circuits
  • Accelerating DNN Training Through Selective Localized Learning (2022), Frontiers in Neuroscience

The scientist has frequently collaborated with other researchers including Sanchari Sen, Xiao Sun, Naigang Wang, Chia-Yu Chen, and Kailash Gopalakrishnan. These collaborations have contributed to publications in notable venues such as:

  • arXiv (Cornell University)
  • IEEE Journal of Solid-State Circuits
  • ACM Transactions on Embedded Computing Systems
  • IEEE Computer Architecture Letters
  • Proceedings of the IEEE

Best Publications

  • PACT: Parameterized Clipping Activation for Quantized Neural Networks

    Jungwook Choi;Zhuo Wang;Swagath Venkataramani;Pierce I.Jen Chuang

  • SALSA: systematic logic synthesis of approximate circuits

    Swagath Venkataramani;Amit Sabne;Vivek Kozhikkottu;Kaushik Roy

  • Quality programmable vector processors for approximate computing

    Swagath Venkataramani;Vinay K. Chippa;Srimat T. Chakradhar;Kaushik Roy

  • Approximate computing and the quest for computing efficiency

    Swagath Venkataramani;Srimat T. Chakradhar;Kaushik Roy;Anand Raghunathan

  • AxNN: energy-efficient neuromorphic systems using approximate computing

    Swagath Venkataramani;Ashish Ranjan;Kaushik Roy;Anand Raghunathan

  • ScaleDeep: A Scalable Compute Architecture for Learning and Evaluating Deep Networks

    Swagath Venkataramani;Ashish Ranjan;Subarno Banerjee;Dipankar Das

  • Substitute-and-simplify: a unified design paradigm for approximate and quality configurable circuits

    Swagath Venkataramani;Kaushik Roy;Anand Raghunathan

  • Hybrid 8-bit Floating Point (HFP8) Training and Inference for Deep Neural Networks

    Xiao Sun;Jungwook Choi;Chia Yu Chen;Naigang Wang

  • A Scalable Multi- TeraOPS Deep Learning Processor Core for AI Trainina and Inference

    Bruce Fleischer;Sunil Shukla;Matthew Ziegler;Joel Silberman

  • ASLAN: synthesis of approximate sequential circuits

    Ashish Ranjan;Arnab Raha;Swagath Venkataramani;Kaushik Roy

  • Accurate and Efficient 2-bit Quantized Neural Networks

    Jungwook Choi;Swagath Venkataramani;Vijayalakshmi Srinivasan;Kailash Gopalakrishnan

  • Scalable-effort classifiers for energy-efficient machine learning

    Swagath Venkataramani;Anand Raghunathan;Jie Liu;Mohammed Shoaib

  • Approximate Storage for Energy Efficient Spintronic Memories

    Ashish Ranjan;Swagath Venkataramani;Xuanyao Fong;Kaushik Roy

  • Multiplier-less Artificial Neurons exploiting error resiliency for energy-efficient neural computing

    Syed Shakib Sarwar;Swagath Venkataramani;Anand Raghunathan;Kaushik Roy

  • Ultra-Low Precision 4-bit Training of Deep Neural Networks

    Xiao Sun;Naigang Wang;Chia-Yu Chen;Jiamin Ni

  • A 7nm 4-Core AI Chip with 25.6TFLOPS Hybrid FP8 Training, 102.4TOPS INT4 Inference and Workload-Aware Throttling

    Ankur Agrawal;Sae Kyu Lee;Joel Silberman;Matthew Ziegler

  • Approximate computing: An integrated hardware approach

    Vinay K. Chippa;Swagath Venkataramani;Srimat T. Chakradhar;Kaushik Roy

  • STAG: spintronic-tape architecture for GPGPU cache hierarchies

    Rangharajan Venkatesan;Shankar Ganesh Ramasubramanian;Swagath Venkataramani;Kaushik Roy

  • Bridging the accuracy gap for 2-bit Quantized Neural Networks (QNN)

    Jungwook Choi;Pierce I.Jen Chuang;Zhuo Wang;Zhuo Wang;Swagath Venkataramani

  • Energy-Efficient Neural Computing with Approximate Multipliers

    Syed Shakib Sarwar;Swagath Venkataramani;Aayush Ankit;Anand Raghunathan

  • RaPiD: AI accelerator for ultra-low precision training and inference

    Swagath Venkataramani;Vijayalakshmi Srinivasan;Wei Wang;Sanchari Sen

Frequent Co-Authors

Anand Raghunathan
Anand Raghunathan Purdue University West Lafayette
Kaushik Roy
Kaushik Roy Purdue University West Lafayette
Vijayalakshmi Srinivasan
Vijayalakshmi Srinivasan IBM (United States)
Leland Chang
Leland Chang IBM Research - Thomas J. Watson Research Center
Michael A. Guillorn
Michael A. Guillorn IBM (United States)
Alper Buyuktosunoglu
Alper Buyuktosunoglu IBM (United States)
Srimat T. Chakradhar
Srimat T. Chakradhar NEC (United States)
Pradip Bose
Pradip Bose IBM (United States)
Jie Liu
Jie Liu Harbin Institute of Technology
Wei Wang
Wei Wang University of California, Los Angeles

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