headshot of Rajkumar Kubendran

Rajkumar Kubendran

Assistant Professor
IEEE Senior Member
rajkumar.ece@pitt.edu
(412) 624-1397
Google Scholar profile ENIGMA Lab Webpage Electrical and Computer Engineering

overview

Rajkumar Kubendran is currently an Assistant Professor at the University of Pittsburgh, Department of Electrical and Computer Engineering. He received his Ph.D. degree from the University of California San Diego, where he worked on energy-efficient Neuromorphic VLSI Computing Systems, spanning from devices to applications. His academic interests include low power analog and mixed signal circuit design with emerging non-volatile memory devices to build event-driven architectures for computer vision and machine learning applications. He has demonstrated prototypes of dynamic vision sensors (DVS) and in-memory compute architectures with some of the best energy-efficiency metrics reported in literature. He received the M.S. degree in Electrical and Computer Engineering from Purdue University in 2012. He has interned with multiple analog and RF design teams in industry, including Intel, IMEC Belgium, MaxLinear and Qualcomm. Currently, his team is working on retina inspired cameras, custom AI hardware for neuromorphic computing and programmable stimulators for biomedical applications.

about

PhD, Electrical and Computer Engineering, University of California, San Diego, 2014 - 2020

MS, Electrical and Computer Engineering, Purdue University, 2009 - 2012

B. Tech, Electronics and Communication Engineering, National Institute of Technology, 2004 - 2008

De, A., Mohammad, H., Wang, Y., Kubendran, R., Das, A.K., & Anantram, M.P. (2023). Performance analysis of DNA crossbar arrays for high-density memory storage applications. Sci Rep, 13(1), 6650.Springer Nature. doi: 10.1038/s41598-023-33004-6.

Richardson, C., Burke, H., Frabitore, Z., Segall, R., Moses-Hampton, M., Kubendran, R., Woeppel, K., Wasan, A.D., & Emerick, T. (2023). Medical Device Development and Start-up Company Formation for Healthcare Entrepreneurs. Pain Med, 24(5), 473-475.Oxford University Press (OUP). doi: 10.1093/pm/pnac152.

Wan, W., Kubendran, R., Schaefer, C., Eryilmaz, S.B., Zhang, W., Wu, D., Deiss, S., Raina, P., Qian, H., Gao, B., Joshi, S., Wu, H., Wong, H.S.P., & Cauwenberghs, G. (2022). A compute-in-memory chip based on resistive random-access memory. Nature, 608(7923), 504-512.Springer Nature. doi: 10.1038/s41586-022-04992-8.

Kim, C., Park, J., Ha, S., Akinin, A., Kubendran, R., Mercier, P.P., & Cauwenberghs, G. (2019). A 3 mm × 3 mm Fully Integrated Wireless Power Receiver and Neural Interface System-on-Chip. IEEE Trans Biomed Circuits Syst, 13(6), 1736-1746.Institute of Electrical and Electronics Engineers (IEEE). doi: 10.1109/TBCAS.2019.2943506.

Kubendran, R., Lee, S., Mitra, S., & Yazicioglu, R.F. (2014). Error correction algorithm for high accuracy bio-impedance measurement in wearable healthcare applications. IEEE Trans Biomed Circuits Syst, 8(2), 196-205.Institute of Electrical and Electronics Engineers (IEEE). doi: 10.1109/TBCAS.2014.2310895.

Venkatachalam, S., Vivekanand, V.S., & Kubendran, R. (2023). Frame of Events: A Low-latency Resource-efficient Approach for Stereo Depth Maps. In 2023 9th International Conference on Automation, Robotics and Applications (ICARA), 00, (pp. 324-328).Institute of Electrical and Electronics Engineers (IEEE). doi: 10.1109/icara56516.2023.10125817.

Vivekanand, V.S., Hashemkhani, S., Venkatachalam, S., & Kubendran, R. (2023). Robot Locomotion Control Using Central Pattern Generator with Non-linear Bio-mimetic Neurons. In 2023 9th International Conference on Automation, Robotics and Applications (ICARA), 00, (pp. 102-106).Institute of Electrical and Electronics Engineers (IEEE). doi: 10.1109/icara56516.2023.10125666.

De, A., Mohammad, H., Wang, Y., Kubendran, R., Das, A.K., & Anantram, M.P. (2022). Modeling and Simulation of DNA Origami based Electronic Read-only Memory. In 2022 IEEE 22nd International Conference on Nanotechnology (NANO), 00, (pp. 385-388).Institute of Electrical and Electronics Engineers (IEEE). doi: 10.1109/nano54668.2022.9928676.

Kubendran, R., Paul, A., & Cauwenberghs, G. (2021). A 256x256 6.3pJ/pixel-event Query-driven Dynamic Vision Sensor with Energy-conserving Row-parallel Event Scanning. In 2021 IEEE Custom Integrated Circuits Conference (CICC), 00, (pp. 1-2).Institute of Electrical and Electronics Engineers (IEEE). doi: 10.1109/cicc51472.2021.9431446.

Kubendran, R., Park, J., Sharma, R., Kim, C., Joshi, S., Cauwenberghs, G., & Ha, S. (2020). A 4.2-pJ/conv 10-b Asynchronous ADC with Hybrid Two-tier Level-crossing Event Coding. In 2020 IEEE International Symposium on Circuits and Systems (ISCAS), 00, (pp. 1-5).Institute of Electrical and Electronics Engineers (IEEE). doi: 10.1109/iscas45731.2020.9180458.

Kubendran, R., Wan, W., Joshi, S., Wong, H.S.P., & Cauwenberghs, G. (2020). A 1.52 pJ/Spike Reconfigurable Multimodal Integrate-and-Fire Neuron Array Transceiver. In International Conference on Neuromorphic Systems 2020, (pp. 1-4).Association for Computing Machinery (ACM). doi: 10.1145/3407197.3407209.

Sengupta, J., Kubendran, R., Neftci, E., & Andreou, A. (2020). High-Speed, Real-Time, Spike-Based Object Tracking and Path Prediction on Google Edge TPU. In 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), 00, (pp. 134-135).Institute of Electrical and Electronics Engineers (IEEE). doi: 10.1109/aicas48895.2020.9073867.

Wan, W., Kubendran, R., Eryilmaz, S.B., Zhang, W., Liao, Y., Wu, D., Deiss, S., Gao, B., Raina, P., Joshi, S., Wu, H., Cauwenberghs, G., & Wong, H.S.P. (2020). A 74 TMACS/W CMOS-RRAM Neurosynaptic Core with Dynamically Reconfigurable Dataflow and In-situ Transposable Weights for Probabilistic Graphical Models. In Digest of Technical Papers - IEEE International Solid-State Circuits Conference, 2020-February, (pp. 498-500).IEEE. doi: 10.1109/ISSCC19947.2020.9062979.

Wan, W., Kubendran, R., Gao, B., Josbi, S., Raina, P., Wu, H., Cauwenberghs, G., & Wong, H.S.P. (2020). A Voltage-Mode Sensing Scheme with Differential-Row Weight Mapping For Energy-Efficient RRAM-Based In-Memory Computing. In 2020 IEEE Symposium on VLSI Technology, 00, (pp. 1-2).Institute of Electrical and Electronics Engineers (IEEE). doi: 10.1109/vlsitechnology18217.2020.9265066.

Kim, C., Ha, S., Akinin, A., Park, J., Kubendran, R., Wang, H., Mercier, P.P., & Cauwenberghs, G. (2017). Design of Miniaturized Wireless Power Receivers for mm-Sized Implants. In 2017 IEEE Custom Integrated Circuits Conference (CICC), 2017-April, (pp. 1-8).Institute of Electrical and Electronics Engineers (IEEE).IEEE. doi: 10.1109/cicc.2017.7993703.

Kim, C., Park, J., Akinin, A., Ha, S., Kubendran, R., Wang, H., Mercier, P.P., & Cauwenberghs, G. (2016). A Fully Integrated 144 MHz Wireless-Power-Receiver-on-Chip with an Adaptive Buck-Boost Regulating Rectifier and Low-Loss H-Tree Signal Distribution. In 2016 IEEE Symposium on VLSI Circuits (VLSI-Circuits), 2016-September, (pp. 1-2).Institute of Electrical and Electronics Engineers (IEEE).IEEE. doi: 10.1109/vlsic.2016.7573492.

Kubendran, R.C., Kim, S., & Yazicioglu, R.F. (2013). Error correction algorithm for high accuracy bio-impedance measurement in wearable healthcare applications. In 2005 IEEE International Symposium on Circuits and Systems (ISCAS), (pp. 1292-1295).Institute of Electrical and Electronics Engineers (IEEE).IEEE. doi: 10.1109/iscas.2013.6572090.

Kubendran, R. (2012). Electromagnetic and Laplace domain analysis of Memristance and Associative learning using Memristive Synapses modeled in SPICE. In 2012 International Conference on Devices, Circuits and Systems (ICDCS), (pp. 622-626).Institute of Electrical and Electronics Engineers (IEEE).IEEE. doi: 10.1109/icdcsyst.2012.6188646.

Kubendran, R., Krishnan, H., Manola, B., John, S.W.M., Chappell, W.J., & Irazoqui, P.P. (2011). A generic miniature multi-feature programmable wireless powering headstage ASIC for implantable biomedical systems. In Annu Int Conf IEEE Eng Med Biol Soc, 2011, (pp. 5617-5620).Institute of Electrical and Electronics Engineers (IEEE).United States. doi: 10.1109/IEMBS.2011.6091359.

Kubendran, R., Cauwenberghs, G., & Mostafa, H. (2021). Compute-in-memory architecture for neural networks. US20210342678A1.

Kubendran, R.C., & Cauwenberghs, G. (2020). QUERY DRIVEN IMAGE SENSING.

Kubendran, R.C., Wang, X., & Zhang, X. (2015). High accuracy measurement of on-chip component parameters.

Research interests

Brain Machine Interfaces
Compute-in-memory Architectures
Dynamic Vision Sensors
Machine Learning
Neuromorphic Engineering