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A multichip neuromorphic system for spike-based visual information processing.

R Jacob Vogelstein1, Udayan Mallik, Eugenio Culurciello

  • 1Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21205, USA. jvogelst@jhu.edu

Neural Computation
|July 26, 2007
PubMed
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This study introduces a novel neuromorphic system for vision processing, featuring a silicon retina and cortex. It demonstrates efficient object recognition using reconfigurable neural networks and dynamic synapse configuration.

Area of Science:

  • Neuromorphic Engineering
  • Computer Vision
  • Artificial Intelligence

Background:

  • Current vision processing systems often lack the efficiency and adaptability of biological systems.
  • Spike-based processing offers a promising alternative for low-power, high-performance computation.
  • Neuromorphic hardware aims to emulate the structure and function of the brain for advanced AI tasks.

Purpose of the Study:

  • To develop and demonstrate a multichip, mixed-signal Very Large Scale Integration (VLSI) system for spike-based vision processing.
  • To implement and test components of an attention-based hierarchical model for cortical object recognition.
  • To showcase the system's capability for dynamic reconfiguration of neural connectivity and synaptic parameters.

Main Methods:

Related Experiment Videos

  • Designed an 80 x 60 pixel neuromorphic retina integrated with a 4800 neuron silicon cortex.
  • Implemented asynchronous routing of neural spike events using a memory-based look-up table for reconfigurable connectivity.
  • Utilized memory storage for dynamic configuration of synaptic parameters like conductance and reversal potential.

    • Main Results:

    • Successfully demonstrated functionality with experimental data on feature coding, salience detection, and foveation.
    • Validated the system's ability to support arbitrary and reconfigurable neural network architectures.
    • Showcased dynamic configuration of synapse circuits, enabling flexible and adaptive processing.

    Conclusions:

    • The developed multichip VLSI system provides a powerful platform for advanced spike-based vision processing.
    • The reconfigurable architecture and dynamic synapse configuration enable efficient emulation of cortical functions.
    • This work advances the development of neuromorphic systems for complex tasks like object recognition.