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Assembly and Characterization of Biomolecular Memristors Consisting of Ion Channel-doped Lipid Membranes

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Published on: March 9, 2019

Reconfigurable Neuromorphic Computing: Materials, Devices, and Integration.

Minyi Xu¹, Xinrui Chen¹, Yehao Guo¹

¹State Key Laboratory of Electronic Thin Film and Integrated Devices, School of Physics, University of Electronic Science and Technology of China, Chengdu, 610054, China.

Advanced Materials (Deerfield Beach, Fla.)

|June 7, 2023

Summary

This summary is machine-generated.

Reconfigurable neuromorphic computing offers superior energy efficiency for artificial general intelligence. This review details advancements in materials, devices, and integration for this brain-inspired technology.

Keywords:

multifunctional devices neuromorphic computing programmable devices reconfigurability reconfigurable integration

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Area of Science:

Neuromorphic Engineering
Materials Science
Computer Science

Background:

Neuromorphic computing promises energy efficiency for artificial general intelligence in the post-Moore era.
Current systems face limitations in interconnections, power consumption, and data handling for complex tasks.
Reconfigurable neuromorphic computing offers a solution by adapting resources for diverse brain-inspired functions.

Purpose of the Study:

To systematically review recent advancements in reconfigurable neuromorphic computing.
To provide a comprehensive overview from material, device, and integration perspectives.
To identify future challenges and opportunities in the field.

Main Methods:

Literature review of recent research in reconfigurable neuromorphic computing.
Categorization of dominant reconfigurability mechanisms at the material and device level.
Analysis of integration strategies and future research directions.

Main Results:

Dominant reconfigurability mechanisms include ion migration, carrier migration, phase transition, spintronics, and photonics.
Developments in material and device engineering enable on-demand resource reallocation.
Integration-level strategies are crucial for realizing advanced neuromorphic systems.

Conclusions:

Reconfigurable neuromorphic computing is a disruptive framework bridging computational primitives.
Further research is needed to overcome challenges in materials, devices, and system integration.
This field holds significant potential for advancing artificial general intelligence.