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8. Neural Network Processors

NNs are models of biological neural networks and some are not, but historically, much of the inspiration for the field of NNs came from the desire to produce artificial systems capable of sophisticated, perhaps "intelligent", computations similar to those that the human brain routinely performs, and thereby possibly to enhance our understanding of the human brain.

Most NNs have some sort of "training" rule whereby the weights of connections are adjusted on the basis of data. In other words, NNs "learn" from examples (as children learn to recognize dogs from examples of dogs) and exhibit some capability for generalization beyond the training data.

NNs normally have great potential for parallelism, since the computations of the components are largely independent of each other. Some people regard massive parallelism and high connectivity to be defining characteristics of NNs, but such requirements rule out various simple models, such as simple linear regression (a minimal feedforward net with only two units plus bias), which are usefully regarded as special cases of NNs.

Some definitions of Neural Network (NN) are as follows:

• According to the DARPA Neural Network Study : A neural network is a system composed of many simple processing elements operating in parallel whose function is determined by network structure, connection strengths, and the processing performed at computing elements or nodes.
• According to Haykin: A neural network is a massively parallel distributed processor that has a natural propensity for storing experiential knowledge and making it available for use. It resembles the brain in two respects:
  • Knowledge is acquired by the network through a learning process.
  • Interneuron connection strengths known as synaptic weights are used to store the knowledge.
• According to Nigrin: A neural network is a circuit composed of a very large number of simple processing elements that are neurally based. Each element operates only on local information. Furthermore each element operates asynchronously; thus there is no overall system clock.
• According to Zurada: Artificial neural systems, or neural networks, are physical cellular systems which can acquire, store, and utilize experiential knowledge.

Visit the following sites for more info on Neural Network Processors

• Omers Neural Network pointers http://www.cs.cf.ac.uk/User/O.F.Rana/neural.html
• FAQ site ftp://ftp.sas.com/pub/neural/FAQ.html
• Automation corp Neural Network Processor hardware

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