非単調ＣＤＭＡニューラルネットを用いた連想記憶イメージセンサの数値的考察

Abstract

We propose a construction of intelligent image sensors that perform associative memory tasks with nonmonotonic CDMA neural networks. The CDMA approach allows implementing Hopfield's associative neural network on a 2-D rectangular grid (chip), while the storage capacity of the chip is increased by employing a nonmonotonic transfer function. The numerical results of the nonmonotonic CDMA neural networks indicated that i) the CDMA network performed the associative memory task well although the multiple-valued outputs were muxed and demuxed by the CDMA, ii) the number of neurons that could be implemented on a 1 cm×1 cm chip was 340 for a 0.6-μm CMOS process, and iii) the storage capacity (the number of stored patterns per number of neurons) was approximately 0.3, which implies a possible development of associative memory systems on image sensors.