Recent Publications in Intelligent Computing

Conference Papers/Presentations

  1. P. Saha, M. Egerstedt, and S. Mukhopadhyay, “Neural Identification for Control”, accepted for publication at IEEE International Conference on Robotics and Automation (ICRA),2021.
  2. 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.
  3. (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
  4. (Invited) K. Samal, M. Wolf, and Mukhopadhyay, “Closed-loop Approach to Perception in Autonomous System” Design, Automation and Test in Europe (DATE), 2021
  5. M. Mukherjee, B. A. Mudassar, M. Lee and S. Mukhopadhyay, “Algorithm-Circuit Cross-layer Control for Digital Pixel Image Sensors,” IEEE Sensors 2020. Best Student Paper Award, Third Place.
  6. B. A. Mudassar, P. Saha and S. Mukhopadhyay, “Uncertainty Characterization in Active Sensor Systems with DNN-based Feedback Control,” IEEE Sensors 2020.
  7. S. Dash and S. Mukhopadhyay, “Hessian-Driven Unequal Parameter Protection for Robust DNN Inference,” IEEE/ACM International Conference on Computer Aided Design (ICCAD), Nov 2020.
  8. K. Samal, M. Wolf, and S. Mukhopadhyay, “Hybridization of Data and Model based Object Detection for Tracking in Flash Lidars,” IEEE International Joint Conference on Neural Network (IJCNN), July 2020
  9. Y. Long, E. Lee, D. Kim, and S. Mukhopadhyay, “Flex-PIM: A Ferroelectric FET based Vector Matrix Multiplication Engine with Dynamical Bitwidth and Floating Point Precision,” IEEE International Joint Conference on Neural Network(IJCNN), July 2020
  10. X. She, P. Saha, D. Kim, Y. Long, and S. Mukhopadhyay, “SAFE-DNN: A Deep Neural Network With Spike Assisted Feature Extraction For Noise Robust Inference,” IEEE International Joint Conference on Neural Network (IJCNN), July 2020
  11. Y. Long, E. Lee, D. Kim and S. Mukhopadhyay, “Q-PIM: A Genetic Algorithm based Flexible DNN Quantization Method and Application to Processing-In-Memory Platform,” IEEE/ACM Design Automation Conference (DAC), July 2020.
  12. E. Lee, B. A. Mudassar, T. Na and S. Mukhopadhyay, “WarningNet: A Deep Learning Platform for Early Warning of Task Failures under Input Perturbation for Reliable Autonomous Platforms,” IEEE/ACM Design Automation Conference (DAC), July 2020.
  13. P. Saha, A. Ali, B. A. Mudassar, Y. Long and S. Mukhopadhyay, “MagNet: Discovering Multi-agent Interaction Dynamics using Neural Network,” IEEE International Conference on Robotics and Automation (ICRA), May 2020.
  14. M. Lee, M. Mukherjee, P. Saha, M. F. Amir, T. Na, and S. Mukhopadhyay, “Effect of Process Variations in Digital Pixel Circuits on the Accuracy of DNN based Smart Sensor,” IEEE International Conference on Artificial Intelligence Circuits and Systems, March 2020.
  15. B. A. Mudassar, P. Saha, E. Gebhardt, D. Samal, T. Na, J. H. Ko, M. Wolf, and S. Mukhopadhyay, “A Camera with Brain: 3D-stacked Sensors with Embedded Machine Learning Based Feedback Control,” GOMACTECH, March 2020.
  16. M. Lee, B. A. Mudassar, T. Na, and S. Mukhopadhyay, “A Spatiotemporal Pre-processing Network for Activity Recognition under Rain,” British Machine Vision Conference(BMVC), Sept. 2019
  17. B. A. Mudassar and S. Mukhopadhyay, “Rethinking Convolutional Feature Extraction for Small Object Detection,” British Machine Vision Conference(BMVC), Sept. 2019
  18. T. Na, M. Lee, B. A. Mudassar, P. Saha, J. H. Ko,and S. Mukhopadhyay, “Mixture of Pre-processing Experts Model for Noise Robust Deep Learning on Resource Constrained Platforms,” IEEE International Joint Conference on Neural Network (IJCNN), July 2019.
  19. B. A. MudassarandS. Mukhopadhyay, “FocalNet – Foveal Attention for Post-processing DNN Outputs,” IEEE International Joint Conference on Neural Network (IJCNN), July 2019.
  20. X. She, Y. Long, and S. Mukhopadhyay, “Improving Robustness of ReRAM-based Spiking Neural Network Accelerator with Stochastic Spike-timing-dependent-plasticity,” IEEE International Joint Conference on Neural Network (IJCNN), July 2019.
  21. Y. Long and S. Mukhopadhyay, “Design of Reliable DNN Accelerator with Un-reliable ReRAM,” Design Automation and Test in Europe(DATE), March 2019.
  22. X. She, Y. Long,and S. Mukhopadhyay, “Fast and Low-Precision Learning in GPU-Accelerated Spiking Neural Network,” Design Automation and Test in Europe(DATE), March 2019.
  23. (Invited) A. Mudassar, P. Saha, Y. Long, M. F. Amir, E. Gebhardt, T. Na, J. H. Ko, M. Wolf and S. Mukhopadhyay, “A Camera With Brain – Embedding Machine Learning In 3d Image Sensor,” Design Automation and Test in Europe(DATE), March 2019.
  24. Y. Long, X. She, and S. Mukhopadhyay, “HybridNet: Integrating Model-based and Data-driven Learning to Predict Evolution of Dynamical Systems,”Conference on Robot Learning, 2018. Paper available in the Proceedings of Machine Learning Research.
  25. J. H. Ko, T. Na, M. F. Amir, and S. Mukhopadhyay, “Edge-Host Partitioning of Deep Neural Networks with Feature Space Encoding for Resource-Constrained Internet-of-Things Platforms,” IEEEInternational Conference on Advanced Video and Signal-based Surveillance(AVSS), 2018.
  26. P. Saha, B. A Mudassar, and S. Mukhopadhyay, “Adaptive Control of Camera Modality with Deep Neural Network-Based Feedback for Efficient Object Tracking,” IEEEInternational Conference on Advanced Video and Signal-based Surveillance (AVSS), 2018.
  27. Y. Long, T. Na, P. Rastogi, K. Rao, A. Khan, S. Yalamanchili, and S.  Mukhopadhyay, “A Ferroelectric FET based Power-efficient Architecture for Data-intensive Computing,” IEEE/ACM International Conference on Computer Aided Design (ICCAD), 2018. 
  28. B. A. Mudassar, J. H. Ko, and S. Mukhopadhyay, “Edge-Cloud Collaborative Processing for Intelligent Internet of Things: A Case Study on Smart Surveillance,” Design Automation Conference (DAC), 2018.
  29. T. Na, J. H. Ko, and S. Mukhopadhyay, “Cascade Adversarial Machine Learning Regularized with a Unified Embedding,” International Conference on Learning Representation (ICLR), 2018
  30. B. A. Mudassar, J. H. Ko, and S. Mukhopadhyay, “An Unsupervised Anomalous Event Detection Framework With Class Aware Source Separation,” IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2018.
  31. T. Na, J. H. Ko, and S. Mukhopadhyay, “Noise-Robust And Resolution-Invariant Image Classification With Pixel-Level Regularization,” IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2018.
  32. Y. Long, X. She, and S. Mukhopadhyay, “Accelerating Biophysical Neural Network Simulation with Region of Interest based Approximation,” Design, Automation, and Test in Europe (DATE), 2018.
  33. (Invited) S. Mukhopadhyay, M. Wolf, M. F. Amir, E. Gebahrdt, J. H. Ko, J. H. Kung, and B. A. Mudassar, “The CAMEL Approach to Stacked Sensor Smart Cameras, Design, Automation, and Test in Europe, 2018.
  34. T. Na, J. H. Ko, and S. Mukhopadhyay, “Cascade Adversarial Machine Learning  Regularized with a Unified Embedding,” Machine Learning and Computer Security Workshop, Neural Information Processing Systems (NIPS-Workshop), 2017.
  35. (Invited) J. H. Ko, Y. Long, M. F. Amir, D. Kim, J. Kung, T. Na, A. Trivedi, and S. Mukhopadhyay, “Energy-Efficient Neural Image Processing for Internet-of-Things Edge Devices,” IEEE International Midwest Symposium on Circuits and Systems (MWSCAS 2017), Aug. 2017.
  36. J. Kung, Y. Long, D. Kim, and S. Mukhoapdhyay, “A Programmable Hardware Accelerator for Simulating Dynamical Systems,” IEEE/ACM International Symposium on Computer Architecture (ISCA), 2017.
  37. J.H. Ko, T. Na, and S. Mukhopadhyay, “Design of An Energy-Efficient Accelerator for Training of Convolutional Neural Networks using Frequency-Domain Computation,”  Design Automation Conference (DAC), 2017.
  38. T. Na, J. H. Ko, J. Kung, and S. Mukhopadhyay, “On-Chip Training of Recurrent Neural Networks with Limited Numerical Precision,” International Joint Conference on Neural Network (IJCNN), May 2017.
  39. J. H. Ko, D. Kim, T. Na, J. Kung, and S. Mukhopadhyay, “Adaptive Weight Compression for Memory-Efficient Neural Networks,” Design, Automation, and Test in Europe (DATE 2017).
  40. J. Kung, Y. Long, and S. Mukhopadhyay, “An Energy-Efficient Physical Platform for Solving Differential Equations,” International Workshop on Post-Moore’s Era Supercomputing (PMES), 2016.
  41. T. Na and S. Mukhopadhyay, “Speeding up Convolutional Neural Network Training with Dynamic Precision Scaling and Flexible Multiplier-Accumulator,” IEEE/ACM International Symposium on Low Power Electronics and Design (ISLPED), 2016
  42. J. H. Kung, D. Kim, and S. Mukhopadhyay, “Dynamic Approximation with Feedback Control for Energy-Efficient Recurrent Neural Network Hardware,” IEEE/ACM International Symposium on Low Power Electronics and Design (ISLPED), 2016.
  43. Y. Long, E. M. Jung, J. Kung and S. Mukhopadhyay, “ReRAM Crossbar based Recurrent Neural Network for Human Activity Detection,” International Joint Conference on Neural Network (IJCNN), 2016.
  44. D. Kim, J. Kung, S. Chai, S. Yalamanchili, and S. Mukhopadhyay,” Neurocube: A Programmable Digital Neuromorphic Architecture with High-Density 3D Memory,” ACM/IEEE International Symposium on Computer Architecture (ISCA), 2016.
  45. A. Trivedi, R. Pandey, H. Liu, S. Datta, and S. Mukhopadhyay, “Gate/Source Overlapped Heterojunction Tunnel FET for non-Boolean Associative Processing with Plasticity,” IEEE International Electron Device Meeting (IEDM), Dec. 2015.
  46. J. H. Kung, D. Kim, and S. Mukhopadhyay, “A Power-Aware Digital Feedforward Neural Network Platform with Backpropagation Driven Approximate Synapses,” IEEE/ACM International Symposium on Low Power Electronic Design (ISLPED), July 2015.
  47. A. Trivedi, M. F. Amir, and S. Mukhopadhyay, “Ultra-low Power Electronics with Si/Ge Tunnel FET,” Design, Automation, and Test in Europe (DATE), March 2014.
  48. A. Trivedi, S. Carlo, and S. Mukhopadhyay, “Exploring Tunnel-FET for Ultra Low Power Analog Applications: A Case Study on Operational Transconductance Amplifier,” Design Automation Conference (DAC), June 2013.

Journal Articles

  1. P. Saha and S. Mukhopadhyay, “A Deep Learning-based Collocation Method for Modeling Unknown PDEs from Sparse Observation”,accepted for publication at IEEE Access.
  2. B. Mudassar, P. Saha, M. Wolf, and S. Mukhopadhyay, “A Task-Driven Feedback Imager with Uncertainty Driven Hybrid Control”, Sensors, April 2021.
  3. Lee, B. Mudassar, and S.Mukhopadhyay, “Adaptive Camera Platform using Deep Learning based Early Warning of Task Failures”, accepted for publication in IEEE Sensors Journal.
  4. M. Lee, B. Mudassar, and S.Mukhopadhyay, “Adaptive Camera Platform using Deep Learning based Early Warning of Task Failures”, accepted for publication in IEEE Sensors Journal, submitted on Sep. 2020.
  5. P. Saha, M. Egerstedt, and S. Mukhopadhyay, “Neural Identification for Control”, accepted for publication at IEEE Robotics and Automation Letters,2021.
  6. 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.
  7. M. Lee, M. Mukherjee, E. Lee, P. Saha, M. Amir, T. Na, and S. Mukhopadhyay, “Cross-Layer Noise Analysis in Smart Digital Pixel Sensors with Integrated Deep Neural Network,” IEEE Journal on Emerging and Selected Topics in Circuits and Systems (JETCAS), vol. 10, no. 4, Dec. 2020, pp. 444-457.
  8. M. Mukherjee, Y. Long, J. Woo, D. Kim, N. M. Rahman, S. Dash, and S. Mukhopadhyay, “A Flexible Precision Multi-Format In-Memory Vector Matrix Multiplication Engine in 65nm CMOS with RF Machine Learning Support,” IEEE Solid State Circuits Letter (SSCL), vol. 3, 2020, pp. 450-453.
  9. D. Kim, X. She, N. M. Rahman, V. Chekuri, and S. Mukhopadhyay, “Processing-In-Memory based On-chip Learning with Spike-Time-Dependent-Plasticity in 65nm CMOS,” IEEE Solid State Circuits Letter (SSCL), vol. 3, 2020, pp. 278-281.
  10. K. Samal, M. Wolf, and S. Mukhopadhyay, “Attention-based Activation Pruning to Reduce Data Movement in Real-time AI: A Case-study on Local Motion Planning in Autonomous Vehicles,” IEEE Journal of Emerging Technology in Circuits and Systems (JETCAS), vol. 10, no. 3, September 2020, pp. 306-319.
  11. P. Saha and S. Mukhopadhyay, “Multispectral Information Fusion with Reinforcement Learning for Object Tracking in IoT Edge Devices,” IEEE Sensors Journal, vol. 20, no. 8, April 2020, pp. 4333 – 4344.
  12. B. Asgari, S. Mukhopadhyay, and S. Yalamanchili, “MAHASIM: Machine-Learning Hardware Acceleration Using a Software-Defined Intelligent Memory System,” Journal Sign Process System, Feb. 2020. https://doi.org/10.1007/s11265-019-01505-1
  13. Y. Long, D. Kim, E. Lee, P. Saha, B. A. Mudassar, X. She, A. I. Khan, and S. Mukhopadhyay, “A Ferroelectric FET based Processing-in-Memory Architecture for DNN Acceleration,” IEEE Journal on Exploratory Devices and Circuits (JXDC), vol. 5, no. 2, December 2019, pp. 113 – 122.
  14. S. MukhopadhyayY. LongB. Mudassar, C. Nair,B. H. Deprospo, H. M. Torun, M. Kathaperumal, V. Smet, D. Kim, S. Yalamanchili, and M. Swaminathan, “Heterogenous Integration for Artificial Intelligence: Challenges and Opportunities,” IBM Journal of Research and Development (IBM J. R&D), vol. 63 , no. 6 , Nov.-Dec. 2019, pp. 4.1-4.23.
  15. B. Mudassar, P. Saha, Y. Long, M. F. Amir, E. Gebhardt, T. Na, J. H. Ko, M. Wolf, and S. Mukhopadhyay, “CAMEL: An Adaptive Camera with Embedded Machine Learning Based Sensor Parameter Control,” IEEE Journal of Emerging Technologies in Circuits and Systems (JETCAS), vo. 9 , no. 3, Sept. 2019, pp. 498-508.
  16. J. H. Ko, D. Kim, T. Na, and S. Mukhopadhyay, “Design and Analysis of a Neural Network Inference Engine based on Adaptive Weight Compression,” IEEE Transactions on Computer Aided Design (TCAD), vol. 38 , no. 1, Jan. 2019, pp. 109 – 121.
  17. Y. Long, T. Na,and S. Mukhopadhyay, “ReRAM based Processing-in-memory Architecture for Recurrent Neural Network Acceleration,” IEEE Transactions on VLSI Systems (TVLSI), vol. 26, no.12, Dec. 2018, pp. 2781-2794.
  18. D. Kim, T. Na, S. Yalamanchili, and S. Mukhopadhyay, “DeepTrain: A Programmable Embedded Platform for Training Deep Neural Networks,” IEEE Transactions on CAD(TCAD), vol. 37, no. 11, Nov. 2018, pp. 2360 – 2370. Presented at ESWEEK, 2018. Nominated for the Best Paper Award.,
  19. J. H. Ko, T. Na, and S. Mukhopadhyay, “An Energy-Quality Scalable Wireless Image Sensor Node for Object-Based Video Surveillance,” IEEE Journal of Emerging and Selected Topics in Circuits and Systems(JETCAS), vol. 8, no. 3, Sept. 2018, pp. 591-602.
  20. M. F. Amir, J. H. Ko, T. Na, D. Kim, and S. Mukhopadhyay, “3D Stacked Image Sensor with Deep Neural Network Computation,” IEEE Sensors Journal (Sensor-J), vol. 18, no. 10,  May, 2018, pp. 4187 – 4199.
  21. J. H. Kung, D. Kim, and S. Mukhopadhyay, “Adaptive Precision Cellular Nonlinear Network,” IEEE Transactions of VLSI Systems (TVLSI), vol. 26, no. 5, May 2018, pp. 841-854.
  22. D. Kim, J. H. Kung, and S. Mukhopadhyay, “A Power-Aware Digital Multilayer Perceptron Accelerator with On-Chip Training based on Approximate Computing,” IEEE Transactions on Emerging Topics in Computing (IEEE TETC), vol. 5, no. 2, April-June 2017, pp. 164-178.
  23. J. Kung, D. Kim, and S. Mukhopadhyay, “On the Impact of Energy-Accuracy Tradeoff in a Digital Cellular Neural Network for Image Processing,” IEEE Transactions on Computer Aided Design (TCAD), vol. 34, no. 7, July 2015, pp. 1070-1081.
  24. A. Trivedi, S. Datta, and S. Mukhopadhyay, “Application of Silicon-Germanium Source Tunnel-FET to enable Ultra-low power Cellular Neural Network based Associative Memory,” IEEE Transactions on Electron Devices (TED), vol. 61, no. 11, Nov. 2014, pp. 3707-3715.
  25. A. Trivedi and S. Mukhopadhyay, “Potential of Ultra-low-power Image Proecssing with Si/Ge Tunneling Nanowires based Cellular Neural Network,” IEEE Transactions on Nanotechnology (TNANO), vol. 13, no. 4, July 2014, pp. 627-629. The top most accessed article in TNANO in every month from August, 2014 to February, 2015.