Recurrent correlation associative memories

T.-D. Chiueh, R.M. Goodman
1991 IEEE Transactions on Neural Networks  
This paper presents a model for a class of high-capacity associative memories. Since they are based on two-layer recurrent neural networks and their operations depend on the correlation measure, we call these associative memories recurrent correlation associative memories (RCAM's). The RCAM's 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 new
more » ... -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. Furthermore, the asymptotic storage capacity of the ECAM with limited dynamic range in its exponentiation nodes is found to be proportional to that dynamic range. This paper also reports a 3 µm CMOS ECAM chip, which bas been designed and fabricated. The prototype chip can store 32 24-bit memory patterns, and its speed is faster than one associative recall operation every 3 µs. An application of the ECAM chip to vector quantization is also described.
doi:10.1109/72.80338 pmid:18276381 fatcat:ufqdbk7qqfhaflktzwyzl2bi6m