1990-05-22
1991-11-12
MacDonald, Allen R.
395 27, G06F 1518
Patent
active
050653393
ABSTRACT:
The neural computing paradigm is characterized as a dynamic and highly parallel computationally intensive system typically consisting of input weight multiplications, product summation, neural state calculations, and complete connectivity among the neurons. Herein is described neural network architecture called SNAP which uses a unique intercommunication scheme within an array structure that provides high performance for completely connected network models such as the Hopfield model. SNAP's packaging and expansion capabilities are addressed, demonstrating SNAP's scalability to larger networks. Each neuron generating a neuron value from a selected set of input function elements and communicating said neuron value back to said set of input function elements. The total connectivity of each neuron to all neurons is accomplished by an orthogonal row-column relationship of neurons where a given multiplier element operates during a first cycle as a row element within an input function to a column neuron, and during a second cycle as a column element within an input function to a row neuron.
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Pechanek Gerald G.
Vassiliadis Stamatis
Augspurger Lynn L.
Beckstrand Shelley M.
International Business Machines - Corporation
MacDonald Allen R.
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