Priyadarshini Panda
Priyadarshini Panda
Assistant Professor, Electrical Engineering, Yale University
Verified email at - Homepage
Cited by
Cited by
Towards spike-based machine intelligence with neuromorphic computing
K Roy, A Jaiswal, P Panda
Nature 575 (7784), 607-617, 2019
Magnetic tunnel junction mimics stochastic cortical spiking neurons
A Sengupta, P Panda, P Wijesinghe, Y Kim, K Roy
Scientific reports 6 (1), 1-8, 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), pp …, 2015
Tree-CNN: A hierarchical deep convolutional neural network for incremental learning
D Roy, P Panda, K Roy
Neural Networks 121, 148-160, 2019
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
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
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
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
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), 1-7, 2017
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
STDP-based unsupervised feature learning using convolution-over-time in spiking neural networks for energy-efficient neuromorphic computing
G Srinivasan, P Panda, K Roy
ACM Journal on Emerging Technologies in Computing Systems (JETC) 14 (4), 44, 2018
Enabling spike-based backpropagation for training deep neural network architectures
C Lee, SS Sarwar, P Panda, G Srinivasan, K Roy
Frontiers in neuroscience 14, 2020
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
Cross-layer approximations for neuromorphic computing: from devices to circuits and systems
P Panda, A Sengupta, SS Sarwar, G Srinivasan, S Venkataramani, ...
Proceedings of the 53rd Annual Design Automation Conference, 98, 2016
ASP: Learning to Forget with Adaptive Synaptic Plasticity in Spiking Neural Networks
P Panda, JM Allred, S Ramanathan, K Roy
IEEE JETCAS doi: 10.1109/JETCAS.2017.2769684, 2017
High-density and robust STT-MRAM array through device/circuit/architecture interactions
KW Kwon, X Fong, P Wijesinghe, P Panda, K Roy
IEEE Transactions on Nanotechnology 14 (6), 1024-1034, 2015
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
Energy efficient neural computing: A study of cross-layer approximations
SS Sarwar, G Srinivasan, B Han, P Wijesinghe, A Jaiswal, P Panda, ...
IEEE Journal on Emerging and Selected Topics in Circuits and Systems 8 (4 …, 2018
Learning to Recognize Actions from Limited Training Examples Using a Recurrent Spiking Neural Model
P Panda, N Srinivasa
Frontiers in Neuroscience 11, 2018
Energy-efficient and improved image recognition with conditional deep learning
P Panda, A Sengupta, K Roy
ACM Journal on Emerging Technologies in Computing Systems (JETC) 13 (3), 1-21, 2017
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