Publications in 2021

Journal Articles

  1. N. E. Miller and S. Mukhopadhyay, “A Quantum Hopfield Associative Memory Implemented on an Actual Quantum Processor,” Nature Scientific Report, vol. 11, 23391, December 2021.
  2. B. Kang, A. Lu, Y. Long, D. Kim, S. Yu, and S. Mukhopadhyay, “Genetic Algorithm based Energy-Aware CNN Quantization for Processing-In-Memory Architecture,” IEEE Journal on Emerging and Selected Topics in Circuits and Systems (JETCAS), vol. 11, no. 4, December 2021, pp. 649-662.
  3. D. Kim, E. Lee, J. Seo, J. Kim, S. K. Lim, and S. Mukhopadhyay, “An SRAM Compiler for Monolithic-3D Integrated Circuit with Carbon Nanotube Transistors,” IEEE Journal of Exploratory Solid-State Computational Devices and Circuits (JXCDC), vol. 7, no. 2, December 2021, pp. 106-114.
  4. J. Kim, V. Chekuri, N. Rahman, M. Dolatsara, H. Torun, M. Swaminathan, S. Mukhopadhyay, and S. K. Lim, “Chiplet/Interposer Co-Design for Power Delivery Network Optimization in Heterogeneous 2.5D ICs”, IEEE Transactions on Components, Packaging and Manufacturing Technology, vol. 11, no. 12, December 2021, pp. 2148-2157.
  5. P. Saha, S. Dash, and S. Mukhopadhyay, “Physics-Incorporated Convolutional Recurrent Neural Networks for Source Identification and Forecasting of Dynamical Systems”,Neural Networks, vol. 144, December 2021, Pages 359-371.
  6. N. E. Miller, Z. Wang, S. Dash, A. I. Khan, and S. Mukhopadhyay “Impact of HKMG and FDSOI FeFET Drain Current Variation in Processing-in-Memory Architecture,” Journal of Materials Research, vol. 36, no. 21, November 2021, pp. 4379–4393.
  7. N. Chawla, A. Singh, H. Kumar, M. Kar, and S. Mukhopadhyay, “Securing IoT Devices using Dynamic Power Management: Machine Learning Approach,” IEEE Internet of Things Journal (IOT-J), vol. 8, no. 22, Nov, 2021, pp. 16379-16394.
  8. B. Chakrabarty and S. Mukhopadhyay, “Characterization of Generalizability of Spike Time Dependent Plasticity trained Spiking Neural Networks,” Frontiers of Neuroscience, vol. 15, October 2021.
  9. B. Chakrabarty, X She, and S. Mukhopadhyay “A Fully Spiking Hybrid Neural Network for Energy-Efficient Object Detection,” IEEE Transactions on Image Processing, vol. 30, October 2021, pp. 9014-9029,
  10. X. She, Y. Long, D. Kim, and S. Mukhopadhyay, “ScieNet: Deep Learning with Spike-assisted Contextual Information Extraction”, Pattern Recognition, vol. 118, October 2021, 108002
  11. X. She and S. Mukhopadhyay, “SPEED: Spiking Neural Network with Event-driven Unsupervised Learning and Near-real-time Inference for Event-based Vision,” IEEE Sensors Journal, vol. 21, no. 18, Sept. 2021, pp. 20578 – 20588.
  12. M. Lee, B. Mudassar, and S.Mukhopadhyay, “Adaptive Camera Platform using Deep Learning based Early Warning of Task Failures”, IEEE Sensors Journal, vol. 21, no. 12, June 2021, pp. 13794 – 13804.
  13. P. Saha, M. Egerstedt, and S. Mukhopadhyay, “Neural Identification for Control”, IEEE Robotics and Automation Letters,2021, vol. 6, no. 3, July 2021, pp. 4648 – 4655.
  14. B. Asgari, S. Mukhopadhyay, and S. Yalamanchili, “MAHASIM: Machine-Learning Hardware Acceleration Using a Software-Defined Intelligent Memory System,” Journal Sign Process System, 93, June 2021, pp. 659–675.
  15. N. Chawla, H. Kumar and S. Mukhopadhyay, “Machine Learning in Wavelet Domain for Electromagnetic Emission Based Malware Analysis”, IEEE Transactions on Information Forensics and Security (TIFS), vol. 16, May 2021, pp. 3426-3441.
  16. P. Saha and S. Mukhopadhyay, “A Deep Learning Approach for Predicting Spatiotemporal Dynamics from Sparsely Observed Data”, IEEE Access, April 2021, pp. 64200 – 64210.
  17. B. Mudassar, P. Saha, M. Wolf, and S. Mukhopadhyay, “A Task-Driven Feedback Imager with Uncertainty Driven Hybrid Control”, Sensors, April 2021.
  18. X. She, S. Dash, D. Kim, and S. Mukhopadhyay, “A Heterogeneous Spiking Neural Network for Unsupervised Learning of Spatiotemporal Patterns”, Frontiers In Neuroscience, January 2021.

Conference Papers

  1. K. Samal, M. Wolf, and S. Mukhopadhyay, “Introspective Closed-Loop Perception for Energy-efficient Sensors,” IEEE International Conference on Advanced Video and Signal-based Surveillance (AVSS), 2021.
  2. N. Miller, Z. Wang, S. Dash, A. Khan, and S. Mukhopadhyay, “Characterization of Drain Current Variations in FeFETs for PIM-Based DNN Accelerators,” IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), 2021.
  3. P. Saha, M. Egerstedt, and S. Mukhopadhyay, “Neural Identification for Control”, accepted for publication at IEEE International Conference on Robotics and Automation (ICRA),2021.
  4. H. Kumar, N. Chawla, and S. Mukhopadhyay, “Towards Improving the Trustworthiness of Hardware based Malware Detector using Online Uncertainty Estimation,” Design Automation Conference, 2021.
  5. J. Woo, K. Jung, and S. Mukhopadhyay, “Efficient On-chip Acceleration of Machine Learning Models for Detection of RF Signal Modulation,” IEEE International Microwave Symposium (IMS), 2021.
  6. E. Huang, C. DeLude, J. Romberg, S. aMukhopadhyay, and M. Swaminathan, “Anisotropic Scatterer Models for Representing RCS of Complex Objects,” IEEE Radar Conference, 2021
  7. (Invited) M. Lee, She, B. Chakraborty, S. Dash, B. A. Mudassar, and S. Mukhopadhyay, “Reliable Edge Intelligence in Unreliable Environment” Design, Automation and Test in Europe (DATE), 2021
  8. (Invited) K. Samal, M. Wolf, and Mukhopadhyay, “Closed-loop Approach to Perception in Autonomous System” Design, Automation and Test in Europe (DATE), 2021