Experimental Demonstration of Supervised Learning in Spiking Neural Networks with Phase-Change Memory SynapsesS. R. NandakumarIrem Boybatet al.2020Scientific Reports
Mixed-Precision Deep Learning Based on Computational MemoryS. R. NandakumarManuel Le Galloet al.2020Frontiers in Neuroscience
Deep learning acceleration based on in-memory computingEvangelos EleftheriouGeethan Karunaratneet al.2019IBM J. Res. Dev
Phase-change memory models for deep learning training and inferenceS. R. NandakumarIrem Boybatet al.2019ICECS 2019
Computational memory-based inference and training of deep neural networksAbu SebastianIrem Boybatet al.2019VLSI Circuits 2019
Multi-ReRAM synapses for artificial neural network trainingIrem BoybatCecilia Giovinazzoet al.2019ISCAS 2019
Impact of conductance drift on multi-PCM synaptic architecturesIrem BoybatS. R. Nandakumaret al.2018NVMTS 2018
A phase-change memory model for neuromorphic computingS. R. NandakumarManuel Le Galloet al.2018Journal of Applied Physics
Neuromorphic computing with multi-memristive synapsesIrem BoybatManuel Le Galloet al.2018Nature Communications
Equivalent-accuracy accelerated neural-network training using analogue memoryStefano AmbrogioPritish Narayananet al.2018Nature