NDLI: Recurrent correlation associative memories

Content Provider	IEEE Xplore Digital Library
Author	Chiueh, T.-D. Goodman, R.M.
Copyright Year	1990
Abstract	A model for a class of high-capacity associative memories is presented. Since they are based on two-layer recurrent neural networks and their operations depend on the correlation measure, these associative memories are called recurrent correlation associative memories (RCAMs). The RCAMs are shown to be asymptotically stable in both synchronous and asynchronous (sequential) update modes as long as their weighting functions are continuous and monotone nondecreasing. In particular, a high-capacity RCAM named the exponential correlation associative memory (ECAM) is proposed. The asymptotic storage capacity of the ECAM scales exponentially with the length of memory patterns, and it meets the ultimate upper bound for the capacity of associative memories. The asymptotic storage capacity of the ECAM with limited dynamic range in its exponentiation nodes is found to be proportional to that dynamic range. Design and fabrication of a 3-mm CMOS ECAM chip is reported. The prototype chip can store 32 24-bit memory patterns, and its speed is higher than one associative recall operation every 3 mu s. An application of the ECAM chip to vector quantization is also described.<>
Sponsorship	IEEE Computational Intelligence Society
Starting Page	275
Ending Page	284
Page Count	10
File Size	738064
File Format	PDF
ISSN	10459227
Volume Number	2
Issue Number	2
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1991-03-01
Publisher Place	U.S.A.
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Associative memory Dynamic range Recurrent neural networks Upper bound Prototypes Neural networks Hopfield neural networks Linear approximation Computer architecture Nonlinear circuits
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Computer Networks and Communications Computer Science Applications Software

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