Reducing the Impact of Phase-Change Memory Conductance Drift on the Inference of large-scale Hardware Neural Networks
Stefano Ambrogio(IBM (United States)), Geoffrey W. Burr(IBM Research - Almaden), M. Wesson(IBM Research - Almaden), Pritish Narayanan(IBM Research - Almaden), Sanjay Kariyappa(IBM Research - Almaden), M. Gallot(IBM Research - Almaden), ChenKang Liu(IBM Research - Almaden), Arvind Kumar(IBM (United States)), Hsinyu Tsai(IBM Research - Almaden), Katherine Spoon(IBM Research - Almaden), A. Chen(IBM Research - Almaden), Charles Mackin(IBM Research - Almaden)
Cited by 73
Related Papers
Recommended Methods to Study Resistive Switching Devices
|Advanced Electronic Materials|2018|649
Emerging neuromorphic devices
|Nanotechnology|2019|312
Neuromorphic Learning and Recognition With One-Transistor-One-Resistor Synapses and Bistable Metal Oxide RRAM
|IEEE Transactions on Electron Devices|2016|250
Unsupervised Learning by Spike Timing Dependent Plasticity in Phase Change Memory (PCM) Synapses
|Frontiers in Neuroscience|2016|246