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VLSI implementable associative memory based on neural network architecture

University of British Columbia

Neural network associative memories based on Hopfield's design have two failure modes. 1) Certain memories are not recallable as they do not lie in a local minimum of the system's Liapunov (energy) function, and 2) the associative memory converges to a non-memory location due to trapping by spurious local minima in the energy function surface. The properties of the first failure mechanism in the Hopfield and five related models were investigated. A new architecture eliminating such failures is proposed. The architecture is fully digital and modular. Furthermore, it is more silicon-area efficient than any of the analyzed models. VLSI circuits built using this architecture will be able to contain a large number of neurons and several circuits may be connected together to further increase capacity.

Text

2010-08-30

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http://hdl.handle.net/2429/27942

Electrical and Computer Engineering

University of British Columbia

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