This paper presents a novel model of optical associative memory using an optoelectronic neurochips, which detects and processes a two-dimensional input image at the same time. The original point of this model is that the optoelectronic neurochips allow direct image processing in terms of parallel input/output interface and parallel neural processing. The operation principle is based on the nonlinear transformation of the input image to the corresponding the point attractor of a fully connected neural network. The learning algorithm is the simulated annealing and the energy of the network state is used as its cost function. The computer simulations show its usefulness and that the maximum number of stored images is 150 in the network with 64 neurons. Moreover, we experimentally demonstrate an optical implementation of the model using the optoelectronic neurochip. The chip consists of two-dimensional array of variable sensitivity photodetectors with 8 16 elements. The experimental results shows that 3 images of size 8 8 were successfully stored in the system. In the case of the input image of size 64 64, the estimated processing speed is 100 times higher than that of the conventional optoelectronic neurochips.

Three dimensional (3-D) optics offers potential advantages to the massively-parallel systems over electronics from the view point of information transfer. The purpose of this paper is to survey some aspects of the 3-D optical interconnection technology for the future massively-parallel computing systems. At first, the state-of-art of the current optoelectronic array devices to build the interconnection networks are described, with emphasis on those based on the semiconductor technology. Next, the principles, basic architectures, several examples of the 3-D optical interconnection systems in neural networks and multiprocessor systems are described. Finally, the issues that are needed to be solved for putting such technology into practical use are summarized.