3 citations · 15 across the 7 of their papers we have counts for
5 papers · 1 filter
Gradient-based Neuromorphic Learning on Dynamical RRAM Arrays
Peng Zhou, Jason K. Eshraghian, Dong-Uk Choi +2
We present MEMprop, the adoption of gradient-based learning to train fully memristive spiking neural networks (MSNNs). Our approach harnesses intrinsic device dynamics to trigger n…
SPICEprop: Backpropagating Errors Through Memristive Spiking Neural Networks
Peng Zhou, Jason K. Eshraghian, Dong-Uk Choi +1
We present a fully memristive spiking neural network (MSNN) consisting of novel memristive neurons trained using the backpropagation through time (BPTT) learning rule. Gradient des…
A Fully Memristive Spiking Neural Network with Unsupervised Learning
Peng Zhou, Dong-Uk Choi, Jason K. Eshraghian +1
We present a fully memristive spiking neural network (MSNN) consisting of physically-realizable memristive neurons and memristive synapses to implement an unsupervised Spiking Time…
Synchronous Unsupervised STDP Learning with Stochastic STT-MRAM Switching
Peng Zhou, Julie A. Smith, Laura Deremo +2
The use of analog resistance states for storing weights in neuromorphic systems is impeded by fabrication imprecision and device stochasticity that limit the precision of synapse w…
Experimental Demonstration of Neuromorphic Network with STT MTJ Synapses
Peng Zhou, Alexander J. Edwards, Fred B. Mancoff +3
We present the first experimental demonstration of a neuromorphic network with magnetic tunnel junction (MTJ) synapses, which performs image recognition via vector-matrix multiplic…