Abstract
In symbolic processing, associative network approaches show promise for solving difficult artificial intelligence problems. [1,2] Optical associative networks, including holographic[3,4] and matrix-vector multiplication [5] architectures, are one of the most attractive approaches toward large-scale associative processing. Optics provides both 2-D parallel interconnection ability between modules and parallel-computing mechanisms for parallel association algorithm. A hybrid optical inference architecture has been proposed. [6] Recently optical architectures for learning and self-organizing neural network are discussed.[7,8]
© 1989 Optical Society of America
PDF ArticleMore Like This
Shirshendu Bhattacharya, Rene Schmogrow, and Mattia Cantono
T4F.2 Optical Fiber Communication Conference (OFC) 2020
Yongli Zhao, Boyuan Yan, Wei Wang, Yi Lin, and Jie Zhang
Tu2E.1 Optical Fiber Communication Conference (OFC) 2019
Yuri Owechko
MD4 Optical Computing (IP) 1989