A comprehensive list of publications is available here.


  • Chankyu Lee, Syed Shakib Sarwar, Priyadarshini Panda, Gopalakrishnan Srinivasan, and Kaushik Roy. Enabling Spike-Based Backpropagation for Training Deep Neural Network Architectures. Frontiers in Neuroscience, 14: 119, 2020.
  • Priyadarshini Panda, QUANOS-Adversarial Noise Sensitivity Driven Hybrid Quantization of Neural Networks. ISLPED ‘20: ACM/IEEE International Symposium on Low Power Electronics and Design, pp. 187–192, 2020.
  • Nitin Rathi, Gopalakrishnan Srinivasan, Priyadarshini Panda, and Kaushik Roy. Enabling Deep Spiking Neural Networks with Hybrid Conversion and Spike Timing Dependent Backpropagation. International Conference on Learning Representations (ICLR), 2020.
  • Priyadarshini Panda, Sai Aparna Aketi, and Kaushik Roy. Toward scalable, efficient, and accurate deep spiking neural networks with backward residual connections, stochastic softmax, and hybridization. Frontiers in Neuroscience, 14: 653, 2020.
  • Abhiroop Bhattacharjee, and Priyadarshini Panda. Re-thinking Non-idealities in Memristive Crossbars for Adversarial Robustness in Neural Networks. arXiv preprint arXiv:2008.11298 (2020).
  • Timothy Foldy-Porto, and Priyadarshini Panda. Activation Density driven Energy-Efficient Pruning in TrainingarXiv preprint arXiv:2002.02949 (Accepted in ICPR 2020).

2019 and prior 

Selected Past Publications (Journal):

  • Kaushik Roy, Akhilesh Jaiswal, and Priyadarshini Panda. Towards spike-based machine intelligence with neuromorphic computing. Nature 575, 607–617 (2019) doi:10.1038/s41586-019-1677-2.  
    • An online tutorial of the article encompassing the perspectives on neuromorphic computing field is available on youtube.
    •  Presentation slides on overview of spiking neural networks is available  here.
  • Fan Zuo*, Priyadarshini Panda*, Michele Kotiuga, Jiarui Li, Mingu Kang, Claudio Mazzoli, Hua Zhou et al. Habituation based synaptic plasticity and organismic learning in a quantum perovskiteNature communications 8, no. 1 (2017): 240 (*Equal author contributions).
  • Priyadarshini Panda, Swagath Venkataramani, Abronil Sengupta, Anand Raghunathan, and Kaushik Roy. Energy-efficient object detection using semantic decompositionIEEE Transactions on Very Large Scale Integration (VLSI) Systems, doi:10.1109/TVLSI.2017.2707077, 25(9):2673–2677, Sept 2017.
  • Priyadarshini Panda, Indranil Chakraborty, and Kaushik Roy. Discretization based Solutions for Secure Machine Learning against Adversarial AttacksIEEE Access (2019).
  • Abhronil Sengupta, Priyadarshini Panda, Parami Wijesinghe, Yusung Kim, and Kaushik Roy. Magnetic tunnel junction mimics stochastic cortical spiking neuronsScientific reports (2016): 30039.
  • Deboleena Roy, Priyadarshini Panda, and Kaushik Roy. Tree-cnn: A hierarchical deep convolutional neural network for incremental learning. arXiv preprint arXiv:1802.05800, 2018, Accepted in Neural Networks (Elsevier), 2019.
  • Chankyu Lee, Priyadarshini Panda, Gopalakrishnan Srinivasan, and Kaushik Roy. Training deep spiking convolutional neural networks with stdp-based unsupervised pre-training followed by supervised finetuningFrontiers in Neuroscience, 12:435, 2018.

Selected Past Publications (Conference):

  • Priyadarshini Panda, Abhronil Sengupta, and Kaushik Roy. Conditional deep learning for energy-efficient and enhanced pattern recognition. In 2016 Design, Automation & Test in Europe Conference & Exhibition (DATE), pp. 475-480. IEEE, 2016.
  • Priyadarshini Panda, and Kaushik Roy. Unsupervised regenerative learning of hierarchical features in spiking deep networks for object recognition. In 2016 International Joint Conference on Neural Networks (IJCNN), pp. 299-306. IEEE, 2016.
  • Priyadarshini Panda and Kaushik Roy. Implicit generative modeling of random noise during training for adversarial robustness. arXiv preprint arXiv:1807.02188, In ICML 2019 - Workshop on Uncertainty and Robustness in Deep Learning.
  • Priyadarshini Panda, Abhronil Sengupta, Syed Shakib Sarwar, Gopalakrishnan Srinivasan, Swagath Venkataramani, Anand Raghunathan, and Kaushik Roy. Cross-layer approximations for neuromorphic computing: From devices to circuits and systems. In 2016 53nd ACM/EDAC/IEEE Design Automation Conference (DAC), pp. 1-6. IEEE, 2016.
  • Aayush Ankit, Abhronil Sengupta, Priyadarshini Panda, and Kaushik Roy. Resparc: A reconfigurable and energy-efficient architecture with memristive crossbars for deep spiking neural networks. In Proceedings of the 54th Annual Design Automation Conference 2017, p. 27. ACM, 2017.

Selected Presentations: