Catherine Schuman

Assistant Professor

University of Tennessee

United States

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About

Catherine Schuman is an Assistant Professor at the University of Tennessee, USA. Her research primarily focuses on neuromorphic computing, including the development of spiking neural networks and hardware-software co-design approaches. She has published recent articles exploring topics like event-based camera systems and random number generators utilizing magnetic tunnel junctions.

Articles (20)

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Collaborators (10)

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Associate Professor

University of Tennessee

UNITED STATES

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Catherine Schuman

Research Interests

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About

Articles (20)

Alleviating the Communication Bottleneck in Neuromorphic Computing with Custom-Designed Spiking Neural Networks

NeuroPong: the event-based camera driven embedded neuromorphic system

The Cart-Pole Application as a Benchmark for Neuromorphic Computing

AI-guided framework for the design of materials and devices for magnetic-tunnel-junction-based true random number generators

Neuromorphic computing at scale

Hardware software co-design for leveraging STDP in a memristive neuroprocessor

Magnetic tunnel junction random number generators applied to dynamically tuned probability trees driven by spin orbit torque

Scaling neural simulations in STACS

Focus on benchmarks for neuromorphic computing

Heterosynaptic plasticity in biomembrane memristors controlled by pH

Probabilistic Neural Computing with Stochastic Devices

A Deep Dive Into the Design Space of a Dynamically Reconfigurable Cryogenic Spiking Neuron

Random Bitstream Generation Using Voltage-Controlled Magnetic Anisotropy and Spin Orbit Torque Magnetic Tunnel Junctions

Unsupervised Digit Recognition Using Cosine Similarity In A Neuromemristive Competitive Learning System

Neuromorphic computing: Challenges from quantum materials to emergent connectivity

Publisher Correction: Opportunities for neuromorphic computing algorithms and applications

Evolutionary vs imitation learning for neuromorphic control at the edge*

Opportunities for neuromorphic computing algorithms and applications

Next-Cycle Optimal Dilute Combustion Control via Online Learning of Cycle-to-Cycle Variability Using Kernel Density Estimators

Next-Cycle Optimal Dilute Combustion Control via Online Learning of Cycle-to-Cycle Variability Using Kernel Density Estimators

Collaborators (10)

Giacomo Indiveri

Maryam Parsa

Axel Hoffmann

Andrew D. Kent

Garrett S. Rose

Alex Frano

Charles Rizzo

Hritom Das

James Plank

Francisco N. Barrera

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