World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
58
Citations
12582
World Ranking
3653
National Ranking
1746

Electronics and Electrical Engineering

D-Index
58
Citations
12892
World Ranking
1863
National Ranking
736

Research.com Recognitions

  • 2018 - Semiconductor Industry Association University Researcher Award
  • 2006 - IEEE Fellow For development of a communication-centric design paradigm for low power systems on a chip.

Overview

Naresh R. Shanbhag is affiliated with the University of Illinois at Urbana-Champaign in the United States. Their research spans multiple fields, predominantly in engineering and computer science. The main areas of study include electrical and electronic engineering, artificial intelligence, computer vision and pattern recognition, neurology, and computational theory and mathematics.

Their work focuses extensively on advanced memory and neural computing, ferroelectric and negative capacitance devices, adversarial robustness in machine learning, semiconductor materials and devices, anomaly detection techniques and applications, advanced neural network applications, and advanced image and video retrieval techniques.

Recent notable publications by Shanbhag include:

  • Benchmarking In-Memory Computing Architectures, 2022, IEEE Open Journal of the Solid-State Circuits Society
  • Comprehending In-memory Computing Trends via Proper Benchmarking, 2022, 2022 IEEE Custom Integrated Circuits Conference (CICC)

Other significant papers related to their research group or frequent collaboration partners are:

  • Deep In-Memory Architectures in SRAM: An Analog Approach to Approximate Computing, 2020, Proceedings of the IEEE
  • A 0.44-μJ/dec, 39.9-μs/dec, Recurrent Attention In-Memory Processor for Keyword Spotting, 2020, IEEE Journal of Solid-State Circuits
  • Signal Processing Methods to Enhance the Energy Efficiency of In-Memory Computing Architectures, 2021, IEEE Transactions on Signal Processing

Frequent co-authors contributing to their research include Hassan Dbouk, Saion K. Roy, Sujan K. Gonugondla, Charbel Sakr, and Ameya D. Patil.

Shanbhag has published in several venues, with a high volume of work appearing in arXiv (Cornell University), as well as multiple publications in the IEEE Journal of Solid-State Circuits, IEEE Transactions on Signal Processing, IEEE Transactions on Circuits and Systems I Regular Papers, and the IEEE Open Journal of the Solid-State Circuits Society.

The scientist has received recognition from professional organizations, including being named an IEEE Fellow in 2006 for the development of a communication-centric design paradigm for low power systems on a chip. Additionally, they were awarded the Semiconductor Industry Association University Researcher Award in 2018.

Best Publications

  • High-throughput LDPC decoders

    M.M. Mansour;N.R. Shanbhag

  • Binodal, wireless epidermal electronic systems with in-sensor analytics for neonatal intensive care

    Ha Uk Chung;Bong Hoon Kim;Jong Yoon Lee;Jungyup Lee

  • High-speed architectures for Reed-Solomon decoders

    D.V. Sarwate;N.R. Shanbhag

  • Soft digital signal processing

    R. Hegde;N.R. Shanbhag

  • Soft-Error-Rate-Analysis (SERA) Methodology

    Ming Zhang;N.R. Shanbhag

  • A 640-Mb/s 2048-bit programmable LDPC decoder chip

    M.M. Mansour;N.R. Shanbhag

  • Energy-efficient signal processing via algorithmic noise-tolerance

    Rajamohana Hegde;Naresh R. Shanbhag

  • A coding framework for low-power address and data busses

    S. Ramprasad;N.R. Shanbhag;I.N. Hajj

  • Sequential Element Design With Built-In Soft Error Resilience

    Ming Zhang;S. Mitra;T.M. Mak;N. Seifert

  • Coding for system-on-chip networks: a unified framework

    S.R. Sridhara;N.R. Shanbhag

  • Low-power VLSI decoder architectures for LDPC codes

    Mohammad M. Mansour;Naresh R. Shanbhag

  • Reliable low-power digital signal processing via reduced precision redundancy

    Byonghyo Shim;S.R. Sridhara;N.R. Shanbhag

  • A soft error rate analysis (SERA) methodology

    Ming Zhang;N. R. Shanbhag

  • A Multi-Functional In-Memory Inference Processor Using a Standard 6T SRAM Array

    Mingu Kang;Sujan K. Gonugondla;Ameya Patil;Naresh R. Shanbhag

  • A 42pJ/decision 3.12TOPS/W robust in-memory machine learning classifier with on-chip training

    Sujan Kumar Gonugondla;Mingu Kang;Naresh Shanbhag

  • Coupling-driven signal encoding scheme for low-power interface design

    Ki-Wook Kim;Kwang-Hyun-Baek;N. Shanbhag;C.L. Liu

  • Energy-efficient soft error-tolerant digital signal processing

    Byonghyo Shim;N.R. Shanbhag

  • Stochastic computation

    Naresh R. Shanbhag;Rami A. Abdallah;Rakesh Kumar;Douglas L. Jones

  • AN ENERGY-EFFICIENT VLSI ARCHITECTURE FOR PATTERN RECOGNITION VIA DEEP EMBEDDING OF COMPUTATION IN SRAM

    Mingu Kang;Min-Sun Keel;Naresh R. Shanbhag;Sean Eilert

  • Toward achieving energy efficiency in presence of deep submicron noise

    R. Hegde;N.R. Shanbhag

  • Coding for systern-on-chip networks: a unified framework

    Srinivasa R. Sridhara;Naresh R. Shanbhag

Frequent Co-Authors

Andrew C. Singer
Andrew C. Singer University of Illinois at Urbana-Champaign
Keshab K. Parhi
Keshab K. Parhi University of Minnesota
Ibrahim N. Hajj
Ibrahim N. Hajj University of Illinois at Urbana-Champaign
Douglas L. Jones
Douglas L. Jones University of Illinois at Urbana-Champaign
Byonghyo Shim
Byonghyo Shim Seoul National University
Ralf Koetter
Ralf Koetter Technical University of Munich
Lav R. Varshney
Lav R. Varshney University of Illinois at Urbana-Champaign
Sasikanth Manipatruni
Sasikanth Manipatruni Intel (United States)
Philip T. Krein
Philip T. Krein University of Illinois at Urbana-Champaign
Ian A. Young
Ian A. Young Intel (United States)

If you think any of the details on this page are incorrect, let us know.

Report an issue

We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:

Related Online Degrees & Career Pathways

For those interested in Electronics and Electrical Engineering, exploring related online degrees can expand career opportunities. Many professionals benefit from accelerated online degree programs for working adults, allowing them to balance education with existing job commitments and gain advanced skills faster.

In addition to technical expertise, degrees in fields like instructional design are gaining popularity. These programs equip students with the ability to create effective educational materials, a valuable skill in training and development roles within engineering firms or tech companies.

Competency-based programs offer another flexible path, especially for those with prior experience. A competency based masters degree allows students to advance by demonstrating their mastery of subject matter rather than following a traditional semester schedule.

Moreover, online education options are increasingly accommodating diverse communities. For example, there are excellent online colleges for military spouses that provide flexibility and support tailored to their unique needs, helping them pursue technical careers alongside their families.

Best Scientists Citing Naresh R. Shanbhag

Trending Scientists

Recently Published Articles