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IJSTR >> Volume 8 - Issue 8, August 2019 Edition

International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616

Journey From Optical Neural Networks To Photonic Chips

[Full Text]



Neha Soni, Enakshi Khular Sharma, Amita Kapoor



Feed Forward Networks, Hopfield Neural Networks, Neuromorphic Chips, Optical Neural Networks, Self Learning Algorithms.



In recent years, there has been a rapid expansion in two fields, photonics and artificial neural networks (ANNs). ANNs based on the basic property of a biological neuron, has become the solution for a wide variety of problems in many fields, such as prediction, modeling, control, recognition, etc. and many of them have reached to the hardware implementation phase. Photonics on the other hand, with several advantageous features like inherent parallelism, high speed of information processing (photon), high capacity data storage, etc. has become a natural choice for researchers for the implementation of ANNs. This combination of photonics and ANNs has resulted in novel realizations of various ANN models. In this paper, we attempt to survey the optical realizations of various neural network models made in last the 30 years. We focus on self organizing neural networks, associative memories, and perceptron neural networks. We also survey the state-of-the-art photonic chips for the realization of ANNs.



[1] Y.S. AbuMostafa, and D. Psaltis, "Optical neural computers," Scientific American 256(3), March 1987: pp. 88-95.
[2] D. Psaltis, D. Brady, X. G. Gu, and S. Lin, "Holography in artificial neural networks," Nature 343.6256, January 1990: pp.325-330.
[3] Soni, Neha, et al. "Face recognition using cloud Hopfield neural network." 2016 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET). IEEE, 2016.
[4] Soni, Neha, et al. "Low-Resolution Image Recognition Using Cloud Hopfield Neural Network." Progress in Advanced Computing and Intelligent Engineering. Springer, Singapore, 2018. 39-46.
[5] Soni, Neha, et al. "Impact of Artificial Intelligence on Businesses: from Research, Innovation, Market Deployment to Future Shifts in Business Models." arXiv preprint arXiv:1905.02092 (2019).
[6] Gulli, Antonio, and Amita Kapoor. (2017) “TensorFlow 1. x Deep Learning Cookbook: Over 90 unique recipes to solve artificial-intelligence driven problems with Python” Packt Publishing Ltd.
[7] Jain, Ankit, Armando Fandango, and Amita Kapoor. (2018) “TensorFlow Machine Learning Projects: Build 13 real-world projects with advanced numerical computations using the Python ecosystem.” Packt Publishing Ltd.
[8] Kapoor Amita (2019) “Hands-On Artificial Intelligence for IoT: Expert machine learning and deep learning techniques for developing smarter IoT systems.” Packt Publishing Ltd.
[9] Soni, Neha, et al. "Success of Optical based Networks for Deep Learning”, 2018, Computing For Sustainable Global Development (INDIACom 2018), IEEE.
[10] Soni, Neha, et al “A survey of the existing optical neural networks”, 2017, Recent Developments in Electronics (NCRDE 2017).
[11] C. Denz, Optical neural networks. Springer Science & Business Media, 2013.
[12] J. Ohta, M. Takahashi, Y. Nitta, S. Tai, K. Mitsunaga and K. Kyum, "GaAs/AlGaAs optical synaptic interconnection device for neural networks," Optics letters 14, 16 August 1989,pp.844-846.
[13] T.T. Lu, F. Yu, and D.A. Gregory. "Self-organizing optical neural network for unsupervised learning," Optical Engineering 29.9,1990,pp.1107-1113.
[14] Heinz, R. A., J. O. Artman, and S. H. Lee. "Matrix multiplication by optical methods." Applied optics 9.9 (1970): 2161-2168.
[15] Tamura, Poohsan N., and James C. Wyant. "Matrix multiplication using coherent optical techniques." Optical Information Processing: Real Time Devices & Novel Techniques. Vol. 83. International Society for Optics and Photonics, 1977.
[16] Liang, Yin-Zhong, and Hua-Kuang Liu. "Optical matrix–matrix multiplication method demonstrated by the use of a multifocus hololens." Optics letters 9.8 (1984): 322-324.
[17] S. Haykin, Neural Network A comprehensive foundation,Neural Networks2004.
[18] P.D. Wasserman, Neural computing. Van Nostrand Reinhold, New York, 1989.
[19] N. Farhat, and P. Demetri, "New approach to optical information processing based on the Hopfield model (A)," Journal of the Optical Society of America A 1,1984: 1296.
[20] N. Farhat, D. Psaltis, A. Prata, and E. Paek, "Optical implementation of the Hopfield model," Applied Optics 24.10, 1985: pp.1469-1475.
[21] I.Shariv, and A. A. Friesem. "All-optical neural network with inhibitory neurons," Optics letters 14.10,1989:pp. 485-487.
[22] S. Jang, S. W. Jung, S.Y. Lee, and S.Y. Shin, "Optical implementation of the Hopfield model for two-dimensional associative memory," Optics letters 13.3, 1988: pp.248-250.
[23] I.Saxena, and E. Fiesler, "Adaptive multilayer optical neural network with optical thresholding," Optical Engineering 34.8, 1995: pp.2435-2440.
[24] T. Kohonen, Self-organization and associative memory. Vol. 8. Springer Science & Business Media, 2012.
[25] Tait, Alexander N., et al. "Neuromorphic Silicon Photonics." arXiv preprint arXiv:1611.02272 (2016).
[26] Tait, Alexander N., et al. "Broadcast and weight: an integrated network for scalable photonic spike processing." Journal of Lightwave Technology 32.21 (2014): 3427-3439.
[27] Tait, Alexander N., et al. "Microring weight banks." IEEE Journal of Selected Topics in Quantum Electronics 22.6 (2016): 312-325.
[28] Shen, Yichen, et al. "Deep learning with coherent nanophotonic circuits." Nature Photonics (2017).