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A novel, compact optoelectronic hardware neural network ar- chitecture based on an optical broadcast scheme is proposed and dem- onstrated. The basic cell in the system is composed of electronic neu- rons that share the same time-distributed, optical broadcast input. The system combines the computational strengths of electronics and the communication strengths of optics, and employs a modular reconfig- urable architecture that is potentially scalable to a very large number of neurons while maintaining compactness. These characteristics are real- ized in an architecture that combines the integration of the processing elements in complementary metal-oxide semiconductor (CMOS) tech- nology with the construction of efficient optical interconnection elements with focusing properties based on multiplexed volume holograms. © 2005 Society of Photo-Optical Instrumentation Engineers. (DOI: 10.1117/1.1902994) Subject terms: optical neural networks; complementary metal-oxide semiconduc- tor sensor arrays; electronic processing; optical interconnects; volume holograms.

We present the first implementation, results, and performance analysis of a vision system whose processing core is a prototype hardware neural network based on an optical broadcast architecture. The system captures an im- age by a CMOS image sensor, compares it with a set of sample patterns (classes), and provides an output that indi- cates the class which the input image corresponds to. Due to the optoelectronic neural processor characteristics, the num- ber of classes can be enlarged without penalty on the opera- tion speed of the system. © 2004 Society of Photo-Optical Instru

A new optoelectronic hardware architecture of a neural network processor is proposed. A basic cell in the system is composed of electronic neurons that share the same time-distributed input; the input is introduced by means of an optical broadcast. The system combines the computational strength of electronics and the communica- tion strength of optics in an optimal manner and it is poten- tially scalable to a very large number of neurons. A descrip- tion of the system is given and the behavior of a first prototype is shown. © 2003 Society of Photo-Optical Instrumentation