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Related Experiment Videos

CrossNets: high-performance neuromorphic architectures for CMOL circuits.

Konstantin Likharev1, Andreas Mayr, Ibrahim Muckra

  • 1Stony Brook University, Stony Brook, New York 11794, USA. klikharev@notes.cc.sunysb.edu

Annals of the New York Academy of Sciences
|February 21, 2004
PubMed
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Hybrid circuits combining silicon MOSFETs and nanowires can extend Moore's Law. Neuromorphic networks, like the proposed CrossNet architecture, offer defect tolerance and high-density processing for advanced computing.

Area of Science:

  • Electronics Engineering
  • Computer Science
  • Materials Science

Background:

  • Continued progress in electronics beyond the 10-nm node requires new paradigms beyond current CMOS technology.
  • Hybrid circuits integrating silicon MOSFETs with molecular electronic devices offer potential for advanced information processing.
  • Molecular devices present challenges such as low voltage gain and component defects.

Purpose of the Study:

  • To propose and evaluate a novel architecture for hybrid neuromorphic circuits.
  • To address the challenges posed by molecular electronic devices in advanced computing architectures.
  • To enable the development of high-density, high-performance computing systems.

Main Methods:

  • Development of distributed crossbar network (CrossNet) architectures.

Related Experiment Videos

  • Integration of silicon MOSFET stacks with parallel nanowires and molecular electronic devices.
  • Neuromorphic network training for pattern recognition and classification.

    • Main Results:

    • CrossNet architectures facilitate high connectivity and component density in neuromorphic circuits.
    • Preliminary estimates suggest a cortex-scale circuit (10^10 neurons, 10^14 synapses) on a 10x10 cm^2 wafer.
    • Achievable cell-to-cell latency of approximately 20 nsec.

    Conclusions:

    • Hybrid CMOL circuits combined with CrossNet neuromorphic architectures can potentially extend Moore's Law.
    • These systems offer significantly higher processing speeds (six orders of magnitude) than biological systems.
    • The proposed approach supports advanced information processing, defect tolerance, and potentially self-evolving systems.