A Multichip Neuromorphic System for Spike-Based Visual Information Processing

Authors:
R. Jacob Vogelstein;Udayan Mallik;Eugenio Culurciello;Gert Cauwenberghs;Ralph Etienne-Cummings
Affiliations:
Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21205, U.S.A. jvogelst@jhu.edu;Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD 21218, U.S.A. udayan@gmail.com;Department of Electrical Engineering, Yale University, New Haven, CT 06511, U.S.A. eugenio.culurciello@yale.edu;Division of Biological Sciences, University of California, San Diego, La Jolla, CA 92093, U.S.A. gert@ucsd.edu;Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD 21218, U.S.A. retienne@jhu.edu
Venue:
Neural Computation
Year:
2007

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Abstract

We present a multichip, mixed-signal VLSI system for spike-based vision processing. The system consists of an 80 × 60 pixel neuromorphic retina and a 4800 neuron silicon cortex with 4,194,304 synapses. Its functionality is illustrated with experimental data on multiple components of an attention-based hierarchical model of cortical object recognition, including feature coding, salience detection, and foveation. This model exploits arbitrary and reconfigurable connectivity between cells in the multichip architecture, achieved by asynchronously routing neural spike events within and between chips according to a memory-based look-up table. Synaptic parameters, including conductance and reversal potential, are also stored in memory and are used to dynamically configure synapse circuits within the silicon neurons.