Keshab Parhi

Professor Keshab Parhi

Keshab Parhi

Distinguished McKnight University Professor, Erwin A. Kelen Chair in Electrical Engineering, Department of Electrical and Computer Engineering


Kenneth H. Keller Hall
Room 6-181
200 Union Street Se
Minneapolis, MN 55455


Center for Neuroengineering
Member, Institute for Engineering in Medicine, University of Minnesota


Ph.D., 1988, University of California, Berkeley, CA, United States
M.S., EE, 1984, University of Pennsylvania, Philadelphia, PA, United States
B.Tech., 1982, Indian Institute of Technology, Kharagpur, India

Professional Background

VLSI architectures for artificial intelligence/machine learning, signal processing, communications and error control coding; hardware security; biomedical signal classification; neuroengineering; DNA computing

Research Interests

Parhi Research Group Site

Research is focused on on all aspects of VLSI signal and image processing starting from algorithm and architecture design to design of digital integrated circuits and computer-aided design tools. Our emphasis is on developing techniques to design architectures and algorithms which can be operated with high speed, or lower area or lower power. Different applications impose different speed-power demands on implementations of an identical algorithm. While video and radar applications require high-speed, wireless and personal communications systems applications require low-power implementations. In addition to studying VLSI implementation styles, we also are studying computer arithmetic implementations and design of CAD tools for high-level synthesis of digital signal processing (DSP) systems and for multiprocessor prototyping and task scheduling of software programmable DSP systems using data-flow graph models.  Very high-speed architectures are designed based on novel use of look-ahead computations, pipelining and retiming. Recent work has addressed pipelined designs for parallel decision feedback equalizers, Tomlinson-Harashima precoders, Viterbi decoders, linear-feedback shift registers, and multi-gigabit transceivers. Significant research has been directed towards parallel and pipelined implementations of turbo decoders, low-power implementations of low-density parity check codes, and crypto-acclerators. Current research on low-power design is based on implementations using overscaled supply voltage and subthreshold circuit design.

Another area of research involves use of advanced signal and image processing techniques and machine learning in classification of biomedical signals. The objective here is to use signal processing for preprocessing and feature extraction and use classifiers for classification. Applications include prediction and detection of seizures in epileptic patients, automated fundus and optical coherence tomography (OCT) imaging analysis for diabetic retinopathy and other ophthalmic abnormalities, and automated screening for mental disorders such as schizophrenia, biorderline personality disorder (BPD) etc. These efforts are in collaborations with various faculty in Medical School at the University of Minnesota. Another effort is directed towards synthesizing various signal processing functions by chemical or molecular reactions. These reactions are mapped to DNA strands. The objective here is to synthesize molecular reactions for a specified signal processing function. The products of these reactions can be used for protein monitoring and drug delivery.

Teaching Subjects
EE 3015 Signals and Systems; EE 3025 Probability and Statistics for Electrical Engineers
EE 4541 Digital Signal Processing
EE 5329 VLSI Digital Signal Processing Systems
EE 5542 Adaptive Digital Signal Processing
EE 5549 Digital Signal Processing Structures for VLSI

Honors and Awards

2022 Charles E. Bowers Faculty Teaching Award 
2021 IEEE Circuits and Systems Society John Choma Education Award 
2020 Association for Computing Machinery (ACM) Fellow for contributions to architectures and design tools for signal processing and networking accelerators
2020 Fellow, National Academy of Inventors (NAI)
2019-2021 IEEE Circuits and Systems Society Distinguished Lecturer 
2017 Fellow, American Association for Advancement of Science (AAAS)
2017 Mac Van Valkenburg Award from the IEEE Circuits and Systems Society 
2013 Distinguished Alumnus Award, Indian Institute of Technology, Kharagpur
2013 Award for Outstanding Contributions to Postbaccalaureate, Graduate, and Professional Education, University of Minnesota
2012 Charles A. Desoer Technical Achievement Award from IEEE Circuits and Systems Society 
2004 Frederick Emmons Terman Award from the American Society of Engineering Education
2003 IEEE Kiyo Tomiyasu Technical Field Award
2000 - present Distinguished McKnight University Professorship
1997- present Edgar F. Johnson Professorship at the University of Minnesota
1996 IEEE Fellow for contributions to the fields of VLSI digital signal processing architectures, design methodologies and tools 
1996-1998 IEEE Circuits and Systems Society Distinguished Lecturer 
1992-1997 National Science Foundation Young Investigator Award
1992-1994 McKnight - Land Grant Professorship at the Univ. of Minnesota



K.K. Parhi, VLSI Digital Signal Processing Systems: Design and Implementation, Wiley, NY 1999

K.K. Parhi and T. Nishitani, Ed., Digital Signal processing for Multimedia Systems, CRC Press, Taylor & Francis Group, Florida, 1999

J.-G. Chung, and K.K. Parhi, Pipelined Lattice and Wave Digital Recursive Filters, Springer, 1996

N.R. Shanbhag, and K.K. Parhi, Pipelined Adaptive Digital Filters, Springer, 1994

R.I. Hartley, and K.K. Parhi, Digit-Serial Computation, Springer, 1995

T. Nishitani and K.K. Parhi, Ed., VLSI Signal Processing VIII, IEEE Press, 1995

J. Fortes, C. Mongenet, K. Parhi, and V. Taylor, Ed., Proceedings 1996 International Conference on Application Specific Systems, Architectures, and Processors, IEEE Computer Society Press, 1996

Selected Publications


2021 and in press

  1. B. Sen, and K.K. Parhi, "Graph-Theoretic Properties of Sub-Graph Entropy," IEEE Signal Processing Letters28, pp. 135-139, 2021 ( Supplementary Information)

  2. K.K. Parhi and N. K. Unnikrishnan, "Correction to "Brain-Inspired Computing: Models and Architectures"," IEEE Open Journal of Circuits and Systems2, pp. 291, Jan. 2021

  3. B. Sen and K.K. Parhi, "Predicting Biological Gender and Intelligence from fMRI via Dynamic Functional Connectivity," IEEE Transactions on Biomedical Engineering68(3), pp. 815-825, March 2021 ( Supplementary Information)

  4. K.K. Parhi, "Teaching Digital Signal Processing by Partial Flipping, Active Learning and Visualization: Keeping Students Engaged With Blended Teaching," IEEE Signal Processing Magazine38(3), pp. 20-29, May 2021 ( Supplementary Material)

  5. B. Sen, K.R. Cullen and K.K. Parhi, "Classification of Adolescent Major Depressive Disorder via Static and Dynamic Connectivity," IEEE Journal of Biomedical and Health Informatics (JBHI), ( Supplementary Information) ePub Dec. 2020


  1. Z. Zhang and K.K. Parhi, "M3U: Minimum Mean Minimum Uncertainty Feature Selection For Multiclass Classification," Springer Journal of Signal Processing Systems (JSPS)92(1), pp. 9-22, Jan. 2020

  2. B. Sen, G.A. Bernstein, B.A. Mueller, K.R. Cullen and K.K. Parhi, "Sub-Graph Entropy based Network Approaches for Classifying Adolescent Obsessive-Compulsive Disorder from Resting-State Functional MRI," Neuroimage: Clinical20, Article 102208, 2020 (Supplementary Information)

  3. C. Cheng and K.K. Parhi, "Fast 2D Convolution Algorithms for Convolutional Neural Networks," IEEE Transactions on Circuits and Systems, Part-I: Regular Papers67(5), pp. 1678-1691, May 2020

  4. X. Liu and K.K. Parhi, "Molecular and DNA Artificial Neural Networks via Fractional Coding," IEEE Transactions on Biomedical Circuits and Systems14(3), pp. 490-503, June 2020 (Supplementary Material)

  5. L. Ge and K.K. Parhi, "Classification using Hyperdimensional Computing: A Review," IEEE Circuits and Systems Magazine20(2), pp. 30-47, June 2020

  6. S.V.S. Avvaru, Z. Zeng and K.K. Parhi, "Homogeneous and Heterogeneous Feed-Forward XOR Physical Unclonable Functions," IEEE Transactions on Information Forensics and Security15, pp. 2485-2498, 2020

  7. K.K. Parhi and N. K. Unnikrishnan, "Brain-Inspired Computing: Models and Architectures," IEEE Open Journal of Circuits and Systems1, pp. 185-204, Nov. 2020

  8. R. Mukherjee, V. Govindan, S. Koteshwara, A. Das, K.K. Parhi, and R.S. Chakraborty, "Probabilistic Hardware Trojan Attacks on Multiple Layers of Reconfigurable Network Infrastructure," Springer Journal of Hardware and Systems Security (HASS)4, pp. 343-360, Nov. 2020

  9. Q. Zhang, Y. Chen, S. Li, X. Zeng and K.K. Parhi, "A High-Performance Stochastic LDPC Decoder Architecture via Correlation Analysis," IEEE Transactions on Circuits and Systems, Part-I: Regular Papers67(12), pp. 5429-5442, Dec. 2020


  1. K.K. Parhi and Y. Liu, "Computing Arithmetic Functions Using Stochastic Logic by Series Expansion," IEEE Transactions on Emerging Technologies in Computing (TETC)7(1), pp. 44-59, March 2019

  2. S. Koteshwara, A. Das and K.K. Parhi, "Architecture Optimization and Performance Comparison of Nonce-Misuse Resistant Authenticated Encryption Algorithms," IEEE Transactions on VLSI Systems27(5), pp. 1053-1066, May 2019

  3. B. Sen, S.-H. Chu and K.K. Parhi, "Ranking Regions, Edges and Classifying Tasks in Functional Brain Graphs by Sub-Graph Entropy," Scientific Reports, Vol. 9, Article 7628, May 2019 ( Supplementary Information)

  4. K.K. Parhi and Z. Zhang, "Discriminative Ratio of Spectral Power and Relative Power Features Derived via Frequency-Domain Model Ratio (FDMR) with Application to Seizure Prediction," IEEE Transactions on Biomedical Circuits and Systems13(4), pp. 645-657, August 2019

  5. H. Bogunovic, F. Venhuizen, S. Klimscha, S. Apostolopoulos, A. Bab-Hadiashar, U. Bagci, M. Faisal Beg, L. Bekalo, Q. Chen, C. Ciller, K. Gopinath, A.K. Gostar, K. Jeon, Z. Ji, S.-H. Kang, D.D. Koozekanani, D. Lu, D. Morley, K.K. Parhi, H.-S. Park, A. Rashno, M. Sarunic, S. Shaikh, J. Sivaswamy, R. Tennakoon, S. Yadav, S. De Zanet, S.M. Waldstein, B.S. Gerendas, C. Klaver, C.I. Sanchez, U. Schmidt-Erfurth, "RETOUCH - The Retinal OCT Fluid Detection and Segmentation Benchmark and Challenge," IEEE Transactions on Medical Imaging38(8), pp. 1858-1874, August 2019

  6. Z. Zhang and K.K. Parhi, "MUSE: Minimum Uncertainty and Sample Elimination Based Binary Feature Selection," IEEE Transactions on Knowledge and Data Engineering (TKDE)31(9), pp. 1750-1764, Sept. 2019

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