| Towards spike-based machine intelligence with neuromorphic computing K Roy, A Jaiswal, P Panda Nature 575 (7784), 607-617, 2019 | 1830 | 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 | 529 | 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 | 518 | 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 | 376 | 2020 |
| Tree-CNN: A hierarchical deep convolutional neural network for incremental learning D Roy, P Panda, K Roy Neural Networks 121, 148-160, 2019 | 319 | 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 | 249 | 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 | 234 | 2015 |
| 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 | 228* | 2021 |
| Magnetic tunnel junction mimics stochastic cortical spiking neurons A Sengupta, P Panda, P Wijesinghe, Y Kim, K Roy Scientific reports 6 (1), 30039, 2016 | 227 | 2016 |
| Revisiting batch normalization for training low-latency deep spiking neural networks from scratch Y Kim, P Panda Frontiers in neuroscience 15, 773954, 2021 | 197 | 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 | 173 | 2018 |
| Exploring neuromorphic computing based on spiking neural networks: Algorithms to hardware N Rathi, I Chakraborty, A Kosta, A Sengupta, A Ankit, P Panda, K Roy ACM Computing Surveys 55 (12), 1-49, 2023 | 157 | 2023 |
| 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 | 147 | 2018 |
| 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 | 145 | 2020 |
| 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 | 143 | 2016 |
| 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 | 136 | 2017 |
| 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 | 134 | 2017 |
| Neuromorphic Data Augmentation for Training Spiking Neural Networks Y Li, Y Kim, H Park, T Geller, P Panda ECCV 2022, 2022 | 117 | 2022 |
| Neural architecture search for spiking neural networks Y Kim, Y Li, H Park, Y Venkatesha, P Panda ECCV 2022, 2022 | 116 | 2022 |
| Optimizing deeper spiking neural networks for dynamic vision sensing Y Kim, P Panda Neural Networks 144, 686-698, 2021 | 116 | 2021 |
Kaushik RoyProfessor of Electrical and Computer Engineering, Purdue Universitypurdue.edu의 이메일 확인됨
