An Analog Neural Computer with Modular Architecture for Real-Time Dynamic Computations

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neural network
neuron
synapse
synaptic time constants
acoustical pattern analysis
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Mueller, Paul
Blackman, David
Chance, Peter
Donham, Christopher
Etienne-Cummings, Ralph
Kinget, Peter
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The paper describes a multichip analog parallel neural network whose architecture, neuron characteristics, synaptic connections, and time constants are modifiable. The system has several important features, such as time constants for time-domain computations, interchangeable chips allowing a modifiable gross architecture, and expandability to any arbitrary size. Such an approach allows the exploration of different network architectures for a wide range of applications, in particular dynamic real-world computations. Four different modules (neuron, synapse, time constant, and switch units) have been designed and fabricated in a 2µm CMOS technology. About 100 of these modules have been assembled in a fully functional prototype neural computer. An integrated software package for setting the network configuration and characteristics, and monitoring the neuron outputs has been developed as well. The performance of the individual modules as well as the overall system response for several applications have been tested successfully. Results of a network for real-time decomposition of acoustical patterns will be discussed.

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1992
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Copyright 1992 IEEE. Reprinted from IEEE Journal of Solid State Circuits, Volume 27, Issue 1, January 1992, pages 82-92. Publisher URL: http://dx.doi.org/10.1109/4.109559 This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of the University of Pennsylvania's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubs-permissions@ieee.org. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.
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