Abhronil Sengupta
Abhronil Sengupta
Monkowski Career Development Assistant Professor of EECS, Penn State University
Verified email at - Homepage
Cited by
Cited by
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
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
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
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
Magnetic tunnel junction mimics stochastic cortical spiking neurons
A Sengupta, P Panda, P Wijesinghe, Y Kim, K Roy
Scientific Reports 6, 30039, 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
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
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
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
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
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
Spin-orbit torque induced spike-timing dependent plasticity
A Sengupta, Z Al Azim, X Fong, K Roy
Applied Physics Letters 106 (9), 093704, 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
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
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
Spin orbit torque based electronic neuron
A Sengupta, SH Choday, Y Kim, K Roy
Applied Physics Letters 106 (14), 143701, 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
Exploring the Connection Between Binary and Spiking Neural Networks
S Lu, A Sengupta
Frontiers in Neuroscience 14, 535, 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
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
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