7 papers
Biologically Realistic Dynamics for Nonlinear Classification in CMOS+X Neurons
Steven Louis, Hannah Bradley, Artem Litvinenko +3
Spiking neural networks encode information in spike timing and offer a pathway toward energy efficient artificial intelligence. However, a key challenge in spiking neural networks…
Temporal magnon-qubit Mach-Zehnder interferometer
Cody Trevillian, Steven Louis, Vasyl Tyberkevych
A temporal magnon-qubit Mach-Zehnder (MZ) interferometer is proposed. The interferometer is based on controllable entanglement of a microwave qubit and a magnonic state, achieved b…
Time-Domain Two-Magnon Interference Enabled by a Tunable Beamsplitter
Cody Trevillian, Steven Louis, Vasyl Tyberkevych
This letter presents a model system for controllable two-magnon interference in the time domain. This two-magnon interference, i.e., a magnonic analog to the photonic Hong-Ou-Mande…
A CMOS+X Spiking Neuron With On-Chip Machine Learning
Steven Louis, Matthew Blake Abramson, Hannah Bradley +8
We present the design and numerical simulation of a spiking neuron in a proof-of-concept model of on-chip machine learning. Built within the CMOS+X framework, the spiking neuron co…
Spintronic Neuron Using a Magnetic Tunnel Junction for Low-Power Neuromorphic Computing
Steven Louis, Hannah Bradley, Cody Trevillian +2
This paper proposes a novel spiking artificial neuron design based on a combined spin valve/magnetic tunnel junction (SV/MTJ). Traditional hardware used in artificial intelligence…
A Physics-Based Circuit Model for Magnetic Tunnel Junctions
Steven Louis, Hannah Bradley, Artem Litvinenko +1
This work presents an equivalent circuit model for Magnetic Tunnel Junctions (MTJs) that accurately captures their magnetization dynamics and electrical behavior. Implemented in LT…