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Swagath Venkataramani

Swagath Venkataramani

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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
13063
National Ranking
5259

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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