Large scale simulations of a spin glass image associative memory
Bruce E. Rosen, James M. Goodwin · 2002
Large scale parallel simulations of a spin glass associative memory are described. As a massively parallel neural network architecture, it is similar to the Boltzmann Machine, but is based on the material and physical characteristics of spin glasses and the use of opto-magnetic control. The system is designed to learn, store, and recall very high dimensional binary patterns vectors. Performance results of the system's autoassociative learning and recall capabilities on a 4996 bit Coca-Cola trademark image are discussed.>