Bipin Rajendran, Ph.D.
Affiliations: | 2006 | Stanford University, Palo Alto, CA |
Area:
Electronics and Electrical EngineeringGoogle:
"Bipin Rajendran"Mean distance: (not calculated yet)
Parents
Sign in to add mentor| R Fabian W. Pease | grad student | 2006 | Stanford | |
| (Low thermal budget processing for sequential three dimensional integrated circuit fabrication.) | ||||
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Publications
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| Nandakumar SR, Le Gallo M, Piveteau C, et al. (2020) Mixed-Precision Deep Learning Based on Computational Memory. Frontiers in Neuroscience. 14: 406 |
| Joshi V, Le Gallo M, Haefeli S, et al. (2020) Accurate deep neural network inference using computational phase-change memory. Nature Communications. 11: 2473 |
| Nandakumar SR, Boybat I, Le Gallo M, et al. (2020) Experimental Demonstration of Supervised Learning in Spiking Neural Networks with Phase-Change Memory Synapses. Scientific Reports. 10: 8080 |
| Xu X, Rajendran B, Anantram MP. (2020) Kinetic Monte Carlo Simulation of Interface-Controlled Hafnia-Based Resistive Memory Ieee Transactions On Electron Devices. 67: 118-124 |
| Rajendran B, Sebastian A, Schmuker M, et al. (2019) Low-Power Neuromorphic Hardware for Signal Processing Applications: A Review of Architectural and System-Level Design Approaches Ieee Signal Processing Magazine. 36: 97-110 |
| Boybat I, Le Gallo M, Nandakumar SR, et al. (2018) Neuromorphic computing with multi-memristive synapses. Nature Communications. 9: 2514 |
| Kulkarni SR, Rajendran B. (2018) Spiking neural networks for handwritten digit recognition-Supervised learning and network optimization. Neural Networks : the Official Journal of the International Neural Network Society. 103: 118-127 |
| Nandakumar SR, Kulkarni SR, Babu AV, et al. (2018) Building Brain-Inspired Computing Systems: Examining the Role of Nanoscale Devices Ieee Nanotechnology Magazine. 12: 19-35 |
| Nandakumar SR, Gallo ML, Boybat I, et al. (2018) A phase-change memory model for neuromorphic computing Journal of Applied Physics. 124: 152135 |
| Babu AV, Lashkare S, Ganguly U, et al. (2018) Stochastic learning in deep neural networks based on nanoscale PCMO device characteristics Neurocomputing. 321: 227-236 |