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Details
Inventors: Vassiliadis, Stamatis; Pechanek, Gerald G.;
Assignee: International Business Machines Corp. (Armonk, NY)
Primary Examiner: Mai; Tan V.
Assistant Examiner:
Attorney, Agent or Firm: Augspurger; Lynn L.

The neural computing paradigm is characterized as a dynamic and highly 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 for a Scalable Neural Array Process (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. The array processor uses a special type of adder tree which computes in a first direction and communicates in a second direction. The adder tree is thus responsive to a compute state and a communication state. The adder tree has the ability to provide a first driver responsive to a compute state for communicating an adder output to a data path and a second driver responsive to the communication state for connecting the data path to the neuron inputs.

DETAILED DESCRIPTION In accordance with the apparatus of the invention, an array processor comprises a plurality of input function elements, with each input function element selectively allocated to a set of neurons, and each neuron including means for generating a neuron value from a selected set of input function elements and for communicating said neuron value back to said selected set of input function elements.
In accordance with the apparatus and method of this invention, the total connectivity of each neuron to all neurons, including itself, is accomplished by an orthogonal relationship of neurons: that is, 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 with an input function to a row neuron.
In accordance with the method of the invention, an array processor comprising orthogonal sets of neurons and a plurality of input function elements, is operated according to the method comprising the steps of (1) operating a first neuron upon a first subset of said input functions to generate and load back into said first subset a neuron value, and (2) allocating each of said first subset of input function elements to one of a set of orthogonal neurons.



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