| Towards spike-based machine intelligence with neuromorphic computing K Roy, A Jaiswal, P Panda Nature 575 (7784), 607-617, 2019 | 1514 | 2019 |
| Enabling spike-based backpropagation for training deep neural network architectures C Lee, SS Sarwar, P Panda, G Srinivasan, K Roy Frontiers in neuroscience 14, 497482, 2020 | 424 | 2020 |
| 2022 roadmap on neuromorphic computing and engineering DV Christensen, R Dittmann, B Linares-Barranco, A Sebastian, ... Neuromorphic Computing and Engineering 2 (2), 022501, 2022 | 416 | 2022 |
| Enabling deep spiking neural networks with hybrid conversion and spike timing dependent backpropagation N Rathi, G Srinivasan, P Panda, K Roy arXiv preprint arXiv:2005.01807, 2020 | 333 | 2020 |
| Tree-CNN: A hierarchical deep convolutional neural network for incremental learning D Roy, P Panda, K Roy Neural Networks 121, 148-160, 2019 | 292 | 2019 |
| Training deep spiking convolutional neural networks with STDP-based unsupervised pre-training followed by supervised fine-tuning C Lee, P Panda, G Srinivasan, K Roy Frontiers in neuroscience 12, 435, 2018 | 226 | 2018 |
| Conditional Deep Learning for Energy-Efficient and Enhanced Pattern Recognition P Panda, A Sengupta, K Roy 2016 Design, Automation & Test in Europe Conference & Exhibition (DATE), pp …, 2015 | 214 | 2015 |
| Magnetic tunnel junction mimics stochastic cortical spiking neurons A Sengupta, P Panda, P Wijesinghe, Y Kim, K Roy Scientific reports 6 (1), 30039, 2016 | 212 | 2016 |
| Domain adaptation without source data Y Kim, D Cho, K Han, P Panda, S Hong IEEE Transactions on Artificial Intelligence 2 (6), 508-518, 2021 | 204* | 2021 |
| Revisiting batch normalization for training low-latency deep spiking neural networks from scratch Y Kim, P Panda Frontiers in neuroscience 15, 773954, 2021 | 166 | 2021 |
| Deep spiking convolutional neural network trained with unsupervised spike-timing-dependent plasticity C Lee, G Srinivasan, P Panda, K Roy IEEE Transactions on Cognitive and Developmental Systems 11 (3), 384-394, 2018 | 157 | 2018 |
| Gabor filter assisted energy efficient fast learning convolutional neural networks SS Sarwar, P Panda, K Roy 2017 IEEE/ACM International Symposium on Low Power Electronics and Design …, 2017 | 135 | 2017 |
| STDP-based pruning of connections and weight quantization in spiking neural networks for energy-efficient recognition N Rathi, P Panda, K Roy IEEE Transactions on Computer-Aided Design of Integrated Circuits and …, 2018 | 134 | 2018 |
| Unsupervised Regenerative Learning of Hierarchical Features in Spiking Deep Networks for Object Recognition P Panda, K Roy 2016 International Joint Conference on Neural Networks (IJCNN), pp. 299-306, 2016 | 133 | 2016 |
| Resparc: A reconfigurable and energy-efficient architecture with memristive crossbars for deep spiking neural networks A Ankit, A Sengupta, P Panda, K Roy Proceedings of the 54th Annual Design Automation Conference 2017, 1-6, 2017 | 123 | 2017 |
| Toward scalable, efficient, and accurate deep spiking neural networks with backward residual connections, stochastic softmax, and hybridization P Panda, SA Aketi, K Roy Frontiers in Neuroscience 14, 653, 2020 | 121 | 2020 |
| Habituation based synaptic plasticity and organismic learning in a quantum perovskite F Zuo, P Panda, M Kotiuga, J Li, M Kang, C Mazzoli, H Zhou, A Barbour, ... Nature communications 8 (1), 240, 2017 | 110 | 2017 |
| Neural architecture search for spiking neural networks Y Kim, Y Li, H Park, Y Venkatesha, P Panda ECCV 2022, 2022 | 95 | 2022 |
| Optimizing deeper spiking neural networks for dynamic vision sensing Y Kim, P Panda Neural Networks 144, 686-698, 2021 | 92 | 2021 |
| Inherent adversarial robustness of deep spiking neural networks: Effects of discrete input encoding and non-linear activations S Sharmin, N Rathi, P Panda, K Roy Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23 …, 2020 | 89 | 2020 |
Kaushik RoyProfessor of Electrical and Computer Engineering, Purdue UniversityVerified email at purdue.edu
