Title :
Recurrent correlation associative memories
Author :
Chiueh, Tzi-Dar ; Goodman, Rodney M.
Author_Institution :
Dept. of Electr. Eng., California Inst. of Technol., Pasadena, CA, USA
fDate :
3/1/1991 12:00:00 AM
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 μs. An application of the ECAM chip to vector quantization is also described
Keywords :
content-addressable storage; correlation methods; neural nets; 24 bit; CMOS; ECAM; RCAM; asymptotic storage capacity; exponential correlation associative memory; neural networks; recurrent correlation associative memories; vector quantization; weighting functions; Associative memory; Computer architecture; Dynamic range; Hopfield neural networks; Linear approximation; Neural networks; Nonlinear circuits; Prototypes; Recurrent neural networks; Upper bound;
Journal_Title :
Neural Networks, IEEE Transactions on
