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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

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Engineered nanoparticle network models for autonomous computing.

Xingfei Wei¹, Yinong Zhao², Yi Zhuang¹

¹Department of Chemistry, Johns Hopkins University, Baltimore, Maryland 21218, USA.

The Journal of Chemical Physics

|July 9, 2021

Summary

Engineered nanoparticles form a network that mimics brain functions like memory. This material shows promise for low-energy, high-capacity computing devices.

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Area of Science:

Materials Science
Computational Neuroscience
Nanotechnology

Background:

Developing brain-mimicking computing devices requires materials with intrinsic synaptic properties.
Engineered nanoparticles (ENPs) interconnected by a polymer network are a promising candidate for neuromorphic computing.

Purpose of the Study:

To investigate the synaptic properties and network behavior of ENP arrays for brain-inspired computing.
To characterize the response of ENP networks to varying environmental and electrical conditions.

Main Methods:

Coarse-grained molecular dynamics (CGMD) simulations were employed to model the behavior of ENP networks.
Analytical lattice network models were used to complement simulation data and predict network characteristics.

Main Results:

Both simulation and analytical models accurately predicted network link formation under different temperatures, free volumes, and electric field strengths.
CGMD simulations revealed hysteretic behavior and synaptic plasticity (short-term and long-term) in response to electric fields.
The ENP networks demonstrated robust non-volatility, unaffected by changes in dielectric constant, temperature, or geometry.

Conclusions:

ENP networks exhibit essential synaptic properties for brain-like data storage and computing.
These materials show potential for creating high-performance, low-energy neuromorphic computing devices.
The observed non-volatility and robustness suggest practical applicability in advanced computing architectures.