Title :
Photonic neurocomputers and learning machines
Author :
Farhat, Nabil H.
Author_Institution :
Dept. of Electr. Eng., Pennsylvania Univ., Philadelphia, PA, USA
Abstract :
Efforts and progress made towards achieving desirable attributes in analog photonic (optoelectronic and/or electron optical) hardware that utilizes primarily incoherent light are reviewed. A hardware implementation of a stochastic Boltzmann learning machine is used as a vehicle for identifying generic issues and clarifying research and development areas for further advancement of the field. The development of architectures and methodologies for learning in self-organizing networks that employ a type of quasi-nonvolatile storage medium called electron trapping material is discussed
Keywords :
integrated optoelectronics; learning systems; neural nets; optical information processing; analogue photonic hardware; architectures; biological neural networks; electron trapping material; electronoptical hardware; incoherent light; optoelectronic hardware; photonic neurocomputers; quasi-nonvolatile storage medium; self-organizing networks; stochastic Boltzmann learning machine; Analog computers; Computer networks; Frequency; High performance computing; High speed optical techniques; Machine learning; Neural networks; Neurons; Optical computing; Physics computing;
Conference_Titel :
Circuits and Systems, 1990., IEEE International Symposium on
Conference_Location :
New Orleans, LA
DOI :
10.1109/ISCAS.1990.112174
