Prototype optoelectronic neural network for artificial vision systems

In this paper we propose of a novel hardware electronic-optoelectronic neural network processor for vision applications. The architecture of the proposed neuroprocessor is based on a hybrid optic and optoelectronic implementation of the system. Some neural operations, like interconnection weight storage and assignment, are done in the electronic domain while the interconnection between processing elements is done optically. By this way we exploit the communication strength of optics and the computational strength of electronics in an optical fashion. The main characteristics of the architecture are that it is fully interconnected, the interconnections are fully programmable, it avoids optical alignment problems, and it is readily scalable to large numbers of pixel neurons. We will describe the architecture, the hardware implementation of a first prototype and its functionality for pattern recognition applications. The neural network models we have implemented on our neuroprocessor have been a basic logic functions operator, a Hopfield network and the matching scores layer of a Hamming network.