World's Best Scientists 2026 revealed!

D-Index & Metrics

Electronics and Electrical Engineering

D-Index
52
Citations
13637
World Ranking
2485
National Ranking
956

Research.com Recognitions

  • 2018 - IEEE Fellow For contributions to energy-efficient and robust computing systems design

Overview

Saibal Mukhopadhyay is affiliated with the Georgia Institute of Technology in the United States. Their research spans multiple areas within engineering and computer science, with particular emphasis on electrical and electronic engineering and artificial intelligence.

The scientist's frequent publication venues include arXiv (Cornell University), IEEE Access, The Journal of the Acoustical Society of America, the 2022 International Joint Conference on Neural Networks (IJCNN), and Frontiers in Neuroscience.

They have contributed to various topics, among which are:

  • Advanced Memory and Neural Computing
  • Neural dynamics and brain function
  • Ferroelectric and Negative Capacitance Devices
  • Neural Networks and Reservoir Computing
  • Neural Networks and Applications
  • Advanced Neural Network Applications
  • CCD and CMOS Imaging Sensors

Mukhopadhyay's main fields of study include:

  • Engineering
  • Computer Science

Their subfields of study further specify focus areas:

  • Electrical and Electronic Engineering
  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Cognitive Neuroscience
  • Signal Processing

Some of the recent papers authored under their name are:

  • "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
  • "Roadmap for unconventional computing with nanotechnology," 2024, Nano Futures
  • "Task-Driven RGB-Lidar Fusion for Object Tracking in Resource-Efficient Autonomous System," 2021, IEEE Transactions on Intelligent Vehicles
  • "A Fully Spiking Hybrid Neural Network for Energy-Efficient Object Detection," 2021, arXiv (Cornell University)
  • "CASCADE ADVERSARIAL MACHINE LEARNING REGULARIZED WITH A UNIFIED EMBEDDING," 2024, TIB Data Manager

Frequent co-authors who have collaborated extensively with Mukhopadhyay include:

  • Biswadeep Chakraborty
  • Priyabrata Saha
  • Daehyun Kim
  • Beomseok Kang
  • Justin Romberg

In recognition of their work, Mukhopadhyay was named an IEEE Fellow in 2018 for contributions to energy-efficient and robust computing systems design.

Best Publications

  • Leakage current mechanisms and leakage reduction techniques in deep-submicrometer CMOS circuits

    K. Roy;S. Mukhopadhyay;H. Mahmoodi-Meimand

  • Modeling of failure probability and statistical design of SRAM array for yield enhancement in nanoscaled CMOS

    S. Mukhopadhyay;H. Mahmoodi;K. Roy

  • A circuit-compatible model of ballistic carbon nanotube field-effect transistors

    A. Raychowdhury;S. Mukhopadhyay;K. Roy

  • Process variation in embedded memories : Failure analysis and variation aware architecture

    Amit Agarwal;Bipul C. Paul;Saibal Mukhopadhyay;Kaushik Roy

  • Gate leakage reduction for scaled devices using transistor stacking

    S. Mukhopadhyay;C. Neau;R.T. Cakici;A. Agarwal

  • Leakage Power Analysis and Reduction for Nanoscale Circuits

    Amit Agarwal;Saibal Mukhopadhyay;Arijit Raychowdhury;Kaushik Roy

  • Estimation of delay variations due to random-dopant fluctuations in nanoscale CMOS circuits

    H. Mahmoodi;S. Mukhopadhyay;K. Roy

  • Statistical design and optimization of SRAM cell for yield enhancement

    S. Mukhopadhyay;H. Mahmoodi;K. Roy

  • Low-power scan design using first-level supply gating

    S. Bhunia;H. Mahmoodi;D. Ghosh;S. Mukhopadhyay

  • Accurate estimation of total leakage current in scaled CMOS logic circuits based on compact current modeling

    Saibal Mukhopadhyay;Arijit Raychowdhury;Kaushik Roy

  • Modeling and estimation of total leakage current in nano-scaled-CMOS devices considering the effect of parameter variation

    Saibal Mukhopadhyay;Kaushik Roy

  • A forward body-biased low-leakage SRAM cache: device, circuit and architecture considerations

    C.H. Kim;Jae-Joon Kim;S. Mukhopadhyay;K. Roy

  • Accurate estimation of total leakage in nanometer-scale bulk CMOS circuits based on device geometry and doping profile

    S. Mukhopadhyay;A. Raychowdhury;K. Roy

  • An energy efficient cache design using spin torque transfer (STT) RAM

    Mitchelle Rasquinha;Dhruv Choudhary;Subho Chatterjee;Saibal Mukhopadhyay

  • Design of Reliable DNN Accelerator with Un-reliable ReRAM

    Yun Long;Xueyuan She;Saibal Mukhopadhyay

  • Device-Optimization Technique for Robust and Low-Power FinFET SRAM Design in NanoScale Era

    A. Bansal;S. Mukhopadhyay;K. Roy

  • Edge-Host Partitioning of Deep Neural Networks with Feature Space Encoding for Resource-Constrained Internet-of-Things Platforms

    Jong Hwan Ko;Taesik Na;Mohammad Faisal Amir;Saibal Mukhopadhyay

  • Modeling and estimation of failure probability due to parameter variations in nano-scale SRAMs for yield enhancement

    S. Mukhopadhyay;H. Mahmoodi-Meimand;K. Roy

  • Process Variations and Process-Tolerant Design

    S. Bhunia;S. Mukhopadhyay;K. Roy

  • Fast and Accurate Analytical Modeling of Through-Silicon-Via Capacitive Coupling

    Dae Hyun Kim;S Mukhopadhyay;Sung Kyu Lim

  • Exploring tunnel-FET for ultra low power analog applications: a case study on operational transconductance amplifier

    Amit Ranjan Trivedi;Sergio Carlo;Saibal Mukhopadhyay

  • ReRAM-Based Processing-in-Memory Architecture for Recurrent Neural Network Acceleration

    Yun Long;Taesik Na;Saibal Mukhopadhyay

Frequent Co-Authors

Kaushik Roy
Kaushik Roy Purdue University West Lafayette
Swarup Bhunia
Swarup Bhunia University of Florida
Hamid Mahmoodi
Hamid Mahmoodi San Francisco State University
Sung Kyu Lim
Sung Kyu Lim Georgia Institute of Technology
Vivek De
Vivek De Intel (United States)
Arijit Raychowdhury
Arijit Raychowdhury Georgia Institute of Technology
Sanu Mathew
Sanu Mathew Intel (United States)
Madhavan Swaminathan
Madhavan Swaminathan Pennsylvania State University
Ching-Te Chuang
Ching-Te Chuang National Yang Ming Chiao Tung University
Chris H. Kim
Chris H. Kim University of Minnesota

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