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Nanotube devices based crossbar architecture: toward neuromorphic computing.

W S Zhao¹, G Agnus, V Derycke

¹CEA, LIST, Embedded Computing Laboratory, Gif-sur-Yvette, France.

|April 7, 2010

Summary

This summary is machine-generated.

Optically gated carbon nanotube devices in a crossbar array offer efficient individual addressing for dense nanodevice integration. This approach simplifies programming for neuromorphic computing architectures.

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Area of Science:

Nanoscience and Nanotechnology
Computer Engineering
Materials Science

Background:

Nanoscale devices like carbon nanotubes and nanowires are crucial for next-generation computing architectures.
Dense integration of nanodevices presents challenges in addressing and interconnection, unlike traditional CMOS technology.
Two-terminal crossbar devices face addressing difficulties due to crosstalk, while three-terminal devices offer individual addressing but limited integration.

Purpose of the Study:

To investigate a novel method for efficiently addressing densely integrated nanodevices in a crossbar geometry.
To explore the potential of optically gated carbon nanotube devices for simplified programming in neuromorphic computing.
To overcome the limitations of existing addressing schemes for nanoscale computing elements.

Main Methods:

Arranging optically gated carbon nanotube devices in a crossbar geometry.
Utilizing shared gate electrodes for addressing the nanodevices.
Demonstrating parallel programming capabilities suitable for neuromorphic applications.

Main Results:

Optically gated carbon nanotube devices enable efficient individual addressing within a crossbar configuration.
The proposed shared gate electrode topology effectively mitigates crosstalk issues.
The architecture supports parallel programming and learning, crucial for neuromorphic computing.

Conclusions:

Optically gated carbon nanotube crossbar arrays provide a viable solution for addressing dense nanodevice integration.
This approach simplifies programming and enhances integration density for future computing architectures.
The findings are particularly relevant for advancing neuromorphic computing systems.