| Going deeper in spiking neural networks: Vgg and residual architectures A Sengupta, Y Ye, R Wang, C Liu, K Roy Frontiers in neuroscience 13, 2019 | 649 | 2019 |
| Spin-transfer torque devices for logic and memory: Prospects and perspectives X Fong, Y Kim, K Yogendra, D Fan, A Sengupta, A Raghunathan, K Roy IEEE Transactions on Computer-Aided Design of Integrated Circuits and …, 2015 | 202 | 2015 |
| Proposal for an all-spin artificial neural network: Emulating neural and synaptic functionalities through domain wall motion in ferromagnets A Sengupta, Y Shim, K Roy IEEE transactions on biomedical circuits and systems 10 (6), 1152-1160, 2016 | 196 | 2016 |
| Magnetic tunnel junction based long-term short-term stochastic synapse for a spiking neural network with on-chip STDP learning G Srinivasan, A Sengupta, K Roy Scientific Reports 6, 29545, 2016 | 181 | 2016 |
| Magnetic tunnel junction mimics stochastic cortical spiking neurons A Sengupta, P Panda, P Wijesinghe, Y Kim, K Roy Scientific Reports 6, 30039, 2016 | 159 | 2016 |
| 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), 475-480, 2016 | 151 | 2016 |
| Toward Fast Neural Computing using All-Photonic Phase Change Spiking Neurons I Chakraborty, G Saha, A Sengupta, K Roy Scientific reports 8 (1), 12980, 2018 | 136 | 2018 |
| Encoding neural and synaptic functionalities in electron spin: A pathway to efficient neuromorphic computing A Sengupta, K Roy Applied Physics Reviews 4 (4), 041105, 2017 | 118 | 2017 |
| Probabilistic deep spiking neural systems enabled by magnetic tunnel junction A Sengupta, M Parsa, B Han, K Roy IEEE Transactions on Electron Devices 63 (7), 2963 - 2970, 2016 | 105 | 2016 |
| Hybrid spintronic-cmos spiking neural network with on-chip learning: Devices, circuits, and systems A Sengupta, A Banerjee, K Roy Physical Review Applied 6 (6), 064003, 2016 | 101 | 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 | 98 | 2017 |
| Spin-orbit torque induced spike-timing dependent plasticity A Sengupta, Z Al Azim, X Fong, K Roy Applied Physics Letters 106 (9), 093704, 2015 | 90 | 2015 |
| An all-memristor deep spiking neural computing system: A step toward realizing the low-power stochastic brain P Wijesinghe, A Ankit, A Sengupta, K Roy IEEE Transactions on Emerging Topics in Computational Intelligence 2 (5 …, 2018 | 87 | 2018 |
| RxNN: A framework for evaluating deep neural networks on resistive crossbars S Jain, A Sengupta, K Roy, A Raghunathan IEEE Transactions on Computer-Aided Design of Integrated Circuits and …, 2020 | 85* | 2020 |
| A vision for all-spin neural networks: A device to system perspective A Sengupta, K Roy IEEE Transactions on Circuits and Systems I: Regular Papers 63 (12), 2267-2277, 2016 | 78 | 2016 |
| Spin orbit torque based electronic neuron A Sengupta, SH Choday, Y Kim, K Roy Applied Physics Letters 106 (14), 143701, 2015 | 76 | 2015 |
| Hierarchical temporal memory based on spin-neurons and resistive memory for energy-efficient brain-inspired computing D Fan, M Sharad, A Sengupta, K Roy IEEE transactions on neural networks and learning systems 27 (9), 1907-1919, 2015 | 72 | 2015 |
| Exploring the Connection Between Binary and Spiking Neural Networks S Lu, A Sengupta Frontiers in Neuroscience 14, 535, 2020 | 68 | 2020 |
| Automatic feature selection of motor imagery EEG signals using differential evolution and learning automata S Bhattacharyya, A Sengupta, T Chakraborti, A Konar, DN Tibarewala Medical & biological engineering & computing 52, 131-139, 2014 | 67 | 2014 |
| Stochastic spiking neural networks enabled by magnetic tunnel junctions: From nontelegraphic to telegraphic switching regimes CM Liyanagedera, A Sengupta, A Jaiswal, K Roy Physical Review Applied 8 (6), 064017, 2017 | 62* | 2017 |
