Reconfigurable Electronic Materials Inspired by Nonlinear Neuron Dynamics (REMIND)

Director

Stanley Williams

Lead Institution

Texas A&M Engineering Experiment Station

Class

2022-2026

Mission

to establish foundational scientific knowledge underpinning the function of massively reconfigurable computing architectures that approach fundamental limits of energy efficiency and speed, enabling real-time learning and embedded intelligence emulative of specific neuronal and synaptic functions of the human brain.

Partner Institutions

Texas A&M Exp Station
NREL
LBNL
SNL-NM

Quick Facts

Single Slide Overview
Technical Summary
https://remind.engr.tamu.edu/

BES Staff Contact

Aaron Holder

Grand Challenges Addressed

Grand
Challenge #1

Grand
Challenge #4

Grand
Challenge #5

Transformative Opportunities Addressed

Mastering Hierarchical Architectures and Beyond-Equilibrium Matter

Trans. Opp. #1

Understanding the Critical Roles of Heterogeneity, Interfaces, and Disorder

Trans. Opp. #2

Revolutionary Advances in Models, Mathematics, Algorithms, Data, and Computing

Trans. Opp. #4

BES Reports Addressed

Highlights

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Applying a voltage to lanthanum strontium manganite (LSMO) can cause it to separate into distinct regions with dramatically different magnetic properties, as illustrated by the red and blue colors in the above figure.

Tuning Magnetism With Voltage Opens a New Path to Neuromorphic Circuits

Experiments show that applied voltage can dramatically alter the magnetic properties of quantum materials.

A metallic line atop a medium biased at the edge of chaos can provide effective negative resistance for time-varying signals, outputting a larger signal compared to its input. The energy for amplification comes from the static bias applied to the medium.

The “Edge of Chaos” Amplifies Signals Without Transistors

Emulating the edge of chaos of axons enables a metal wire to overcome its resistance without cooling, thereby amplifying signals flowing inside of it.