Accurate deep neural network inference using computational phase-change memory
- Vinay Joshi
- Manuel Le Gallo
- et al.
- 2020
- Nature Communications
Paper
Publications
36 results for Bipin Rajendran
Experimental Demonstration of Supervised Learning in Spiking Neural Networks with Phase-Change Memory Synapses
- S. R. Nandakumar
- Irem Boybat
- et al.
- 2020
- Scientific Reports
PaperMixed-Precision Deep Learning Based on Computational Memory
- S. R. Nandakumar
- Manuel Le Gallo
- et al.
- 2020
- Frontiers in Neuroscience
PaperLow-Power Neuromorphic Hardware for Signal Processing Applications: A review of architectural and system-level design approaches
- Bipin Rajendran
- Abu Sebastian
- et al.
- 2019
- IEEE SPM
PaperPhase-change memory models for deep learning training and inference
- S. R. Nandakumar
- Irem Boybat
- et al.
- 2019
- ICECS 2019
Conference paperComputational memory-based inference and training of deep neural networks
- Abu Sebastian
- Irem Boybat
- et al.
- 2019
- VLSI Circuits 2019
Conference paperBuilding next-generation AI systems: Co-optimization of algorithms, architectures, and nanoscale memristive devices
- Bipin Rajendran
- Abu Sebastian
- et al.
- 2019
- IMW 2019
Conference paperImpact of conductance drift on multi-PCM synaptic architectures
- Irem Boybat
- S. R. Nandakumar
- et al.
- 2018
- NVMTS 2018
Conference paperA phase-change memory model for neuromorphic computing
- S. R. Nandakumar
- Manuel Le Gallo
- et al.
- 2018
- Journal of Applied Physics
PaperNeuromorphic computing with multi-memristive synapses
- 2018
- Nature Communications
Paper