2 citations · 2 across the 4 of their papers we have counts for
6 papers
Supervised Learning in Temporally-Coded Spiking Neural Networks with Approximate Backpropagation
Andrew Stephan, Brian Gardner, Steven J. Koester +1
In this work we propose a new supervised learning method for temporally-encoded multilayer spiking networks to perform classification. The method employs a reinforcement signal tha…
Spin-Hall MTJ Cells for Intra-Column Competition in Hierarchical Temporal Memory
Andrew W. Stephan, Steven J. Koester
We propose a dedicated winner-take-all circuit to efficiently implement the intra-column competition between cells in Hierarchical Temporal Memory which is a crucial part of the al…
SHE-MTJ Circuits for Convolutional Neural Networks
Andrew W. Stephan, Steven J. Koester
We report the performance characteristics of a notional Convolutional Neural Network based on the previously-proposed Multiply-Accumulate-Activate-Pool set, an MTJ-based spintronic…
Nonvolatile Spintronic Memory Cells for Neural Networks
Andrew W. Stephan, Qiuwen Lou, Michael Niemier +2
A new spintronic nonvolatile memory cell analogous to 1T DRAM with non-destructive read is proposed. The cells can be used as neural computing units. A dual-circuit neural network…
Convolutional Neural Networks Utilizing Multifunctional Spin-Hall MTJ Neurons
Andrew W. Stephan, Steven J. Koester
We propose a new network architecture for standard spin-Hall magnetic tunnel junction-based spintronic neurons that allows them to compute multiple critical convolutional neural ne…
Benchmarking Inverse Rashba-Edelstein Magnetoelectric Devices for Neuromorphic Computing
Andrew W. Stephan, Jiaxi Hu, Steven J. Koester
We propose a new design for a cellular neural network with spintronic neurons and CMOS-based synapses. Harnessing the magnetoelectric and inverse Rashba-Edelstein effects allows na…