Keshab Parhi


Research Area: Communications, Signal Processing and Networking; Computer Engineering, VLSI and Circuits; Biomedical and Biological Computational Methods, Devices, and System

6-181 Keller Hall

Area of Expertise:

VLSI Architectures for Signal Processing, Communications and Error Control Coding; Hardware Security; Biomedical Signal Classification; Neuroengineering; DNA Computing

Faculty Affiliations:

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


Distinguished Alumnus Award, Indian Institute Technology, Kharagpur, 2013

Award for Outstanding Contributions to Postbaccalaureate, Graduate,
and Professional Education, University of Minnesota, 2013

Charles A. Desoer Technical Achievement Award from IEEE Circuits and
Systems Society, 2012

Edgar F. Johnson Professor in Electronic Communications

Editor-in-Chief, IEEE Trans. Circuits and Systems-I:Regular Papers, 2004-2005

ASEE Frederick Emmons Terman Award, 2004

IEEE Kiyo Tomiyasu Technical Field Award, 2003

IEEE W.R.G. Baker Prize Paper Award, 2001

IEEE Circuits and Systems Society Golden Jubilee Medal, 1999

Fellow, IEEE, 1996

National Science Foundation Young Investigator Award, 1992-1997

IEEE Browder J. thompson Memorial Prize Paper Award, 1991


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.

See my personal web page for more details.


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

K.K. Parhi and M. Ayinala, “Low-Complexity Welch Power Spectral Density Computation,” IEEE Trans. Circuits and Systems-I: Regular Papers, 61(1), pp. 172-182, Jan. 2014

B. Yuan and K.K. Parhi, “Low-Latency Successive-Cancellation Polar Decoder Architectures using 2-bit Decoding,” IEEE Trans. Circuits and Systems-I: Regular Papers, 61(4), pp. 1241-1254, Apr. 2014

Y. Lao, Q. Tang, C.H. Kim and K.K. Parhi, “Beat Frequency Detector based High-Speed TRNGs: Statistical Modeling and Analysis,” ACM Journal on Emerging Technologies in Computing Systems (JETC), 13(1), Article 9, Dec. 2016

Y. Lao and K.K. Parhi, “Obfuscating DSP Circuits via High-Level Transformations,” IEEE Transactions on VLSI Systems, 23(5), pp. 819-830, May 2015

Z. Zhang and K.K. Parhi, “Low-Complexity Seizure Prediction From iEEG/sEEG using Spectral Power and Ratios of Spectral Power,” IEEE Transactions on Biomedical Circuits and Systems, 10(3), pp. 693-706, June 2016

T. Xu, K.R. Cullen, B. Mueller, M.W. Schreiner, K.O. Lim, S.C. Schulz, and K.K. Parhi, “Network Analysis of Functional Brain Connectivity in Borderline Personality Disorder Using Resting-State fMRI,” NeuroImage: Clinical, 11, pp. 302-315, 2016

T. Xu, M. Stephane and K.K. Parhi, “Abnormal Neural Oscillations in Schizophrenia Assessed by Spectral Power Ratio of MEG during Word Processing,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, 24(11), pp. 1148-1158, Nov. 2016

S. Roychowdhury, D.D. Koozekanani and K.K. Parhi, “DREAM: Diabetic Retinopathy Analysis using Machine Learning,” IEEE Journal of Biomedical and Health Informatics, 18(5), pp. 1717-1728, Sept. 2014

H. Jiang, S.A. Salehi, M.D. Riedel, and K.K. Parhi, “Discrete-Time Signal Processing with DNA” American Chemical Society (ACS) Synthetic Biology, 2(5), pp. 245-254, 2013

S.A. Salehi, H. Jiang, M.D. Riedel, and K.K. Parhi, “Molecular Sensing and Computing Systems (Invited Paper),” IEEE Transactions on Molecular, Biological, and Multi-Scale Communications, 1(3), pp. 249-264, Sept. 2015


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