Learning with Deep Photonic Neural Networks

Leelar, BS and Shivaleela, ES and Srinivas, T (2018) Learning with Deep Photonic Neural Networks. In: 3rd IEEE Workshop on Recent Advances in Photonics, WRAP 2017, 18 - 19 December 2017, Hyderabad.

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Abstract

Artificial Neural Networks (ANNs) are computational models used in Machine Learning (ML), based on a large collection of connected simple units called artificial neurons, loosely analogous to axons in a biological brain. Most of the Neural Networks (NNs) work in classical architecture and a few hardware implementations with optical components have been proposed, which facilitates real-time parallel processing of massive data sets. While these Optical Neural Networks (ONNs) are faster and comparable in efficiency with the classical NNs, they lack in scalability and flexibility. We have proposed a scalable and reconfigurable photonic lattice based NN, similar to feedforward neural network, where photonic lattice represents the layer of the network. The superposition property of quantum computations allows photons to process massive data sets faster than electronic circuits. We explicitly tried to exploit quantum coherence for learning. Hardware implementations put restrictions on the weight space of photon as negative weights are not possible, therefore we have modified the Backpropagation algorithm for optimizing the weights of the network to manipulate the quantum states. The proposed architecture is theoretically scalable as any number of photonic lattice can be included and provides a platform to get benefit of photonic lattices with different properties. It can be used in understanding the learning behaviour of NNs in a constrained environment.

Item Type:	Conference Paper
Publication:	WRAP 2017 - Workshop on Recent Advances in Photonics
Publisher:	Institute of Electrical and Electronics Engineers Inc.
Additional Information:	The copyright for this article belongs to the Institute of Electrical and Electronics Engineers Inc.
Keywords:	Backpropagation algorithms; Computer hardware; Data handling; Deep neural networks; Feedforward neural networks; Hardware; Metadata; Network architecture; Network layers; Neural networks; Neurons; Optical lattices; Photonics; Photons; Quantum computers; Quantum optics, Density matrix; Hardware implementations; Neural networks (NNS); Optical neural networks; Photonic network; Proposed architectures; Quantum Computing; Reconfigurable photonics, Big data
Department/Centre:	Division of Electrical Sciences > Electrical Communication Engineering
Date Deposited:	05 Aug 2022 10:11
Last Modified:	05 Aug 2022 10:11
URI:	https://eprints.iisc.ac.in/id/eprint/75370

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