All-optical neural network with nonlinear activation functions

Zuo, Ying; Li, Bohan; Zhao, Yongqiang; Jiang, Yue; Chen, You-Chiuan; Chen, Peng; Jo, Gyu-Boong; Liu, Junwei; Du, Shengwang

doi:10.1364/optica.6.001132

Cited by 336 publications

(187 citation statements)

References 49 publications

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“…This is the first time that integrated optical neural networks have been developed. Furthermore, Ying Zuo et aldemonstrates an AONN with adjustable linear operations using SLM and Fourier lenses to achieve such linear process [37]. In the process of linear operation, the power of incident light in different regions of SLM represents different input nodes.…”

Section: A Use Slm To Build Onnmentioning

confidence: 99%

A Review of Optical Neural Networks

et al. 2020

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Optical neural network can process information in parallel by using the technology based on free-space and integrated platform. Over the last half century, the development of integrated circuits has been limited by Moore's law. We know that neural network is based on the digital computer for successive calculation, most of which cannot be made into real-time processing system. Therefore, it is necessary to develop ONN for real-time processing and device miniaturization. In this paper, we review the progress of optical neural networks. Firstly, based on the principle of artificial neural networks, we elaborate the essence of optical matrix multiplier for linear operation. Then we introduce the optical neural network achieved by free-space optical interconnection and waveguide optical interconnection. Finally we talk about the nonlinearity in optical neural networks. With the gradual maturity of nanotechnology and the rapid advancement of silicon photonic integrated circuits, the progress of integrated photonic neural network has been promoted. Therefore, the construction of optical neural network on the future integrated photonic platform has potential application value. INDEX TERMS Optical neural networks, optical waveguides, free-space optical interconnection, optical non-linearities, optical elements, photonic integrated circuits.

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Section: A Use Slm To Build Onnmentioning

confidence: 99%

A Review of Optical Neural Networks

et al. 2020

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“…Thus, analogue optical technology allows to implement artificial neural networks directly in hardware, with data encoded in pulses of light and neurons made from optical elements, such as lenses, prisms, beam splitters, waveguides and spatial light modulators (SLMs), see Figure 6a. In particular, SLMs are used for algebraic operations, including matrix multiplication with a specific phase mask design [24]. Recently, another approach to realise optical neural networks was based on Mach-Zehnder interferometers (MZIs) to calculate matrix products [25,26], see Figure 6b.…”

Section: All-optical Neural Networkmentioning

confidence: 99%

“…Most promising nonlinear effects are based on harmonics generation, phase conjugation, optical limiter, and bistable response. Recently, researchers from The Hong Kong University of Science and Technology proposed a new approach based on cold atoms exhibiting electromagnetic induced transparency effect to implement the nonlinear activation function [24]. Importantly, it requires very weak laser power and is based on nonlinear quantum interference.…”

Section: All-optical Neural Networkmentioning

confidence: 99%

Deep Learning Enabled Nanophotonics

Huang¹,

Xu²,

Miroshnichenko³

2020

Advances and Applications in Deep Learning

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Deep learning has become a vital approach to solving a big-data-driven problem. It has found tremendous applications in computer vision and natural language processing. More recently, deep learning has been widely used in optimising the performance of nanophotonic devices, where the conventional computational approach may require much computation time and significant computation source. In this chapter, we briefly review the recent progress of deep learning in nanophotonics. We overview the applications of the deep learning approach to optimising the various nanophotonic devices. It includes multilayer structures, plasmonic/ dielectric metasurfaces and plasmonic chiral metamaterials. Also, nanophotonic can directly serve as an ideal platform to mimic optical neural networks based on nonlinear optical media, which in turn help to achieve high-performance photonic chips that may not be realised based on conventional design method.

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“…Among them, photonic devices represent a promising technological platform due to their fast switching time, high bandwidth and low crosstalks [18]. For neural networks, for instance, first proof-of-principle demonstrations on optical platforms have already been studied [19,20] and experimentally tested [21,22]. Inspired by the outstanding success of both RL and ASICs, here we present a novel photonic architecture for the implementation of active learning agents.…”

Section: Introductionmentioning

confidence: 99%

Photonic architecture for reinforcement learning

et al. 2020

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The last decade has seen an unprecedented growth in artificial intelligence and photonic technologies, both of which drive the limits of modern-day computing devices. In line with these recent developments, this work brings together the state of the art of both fields within the framework of reinforcement learning. We present the blueprint for a photonic implementation of an active learning machine incorporating contemporary algorithms such as SARSA, Q-learning, and projective simulation. We numerically investigate its performance within typical reinforcement learning environments, showing that realistic levels of experimental noise can be tolerated or even be beneficial for the learning process. Remarkably, the architecture itself enables mechanisms of abstraction and generalization, two features which are often considered key ingredients for artificial intelligence. The proposed architecture, based on single-photon evolution on a mesh of tunable beamsplitters, is simple, scalable, and a first integration in quantum optical experiments appears to be within the reach of near-term technology.OPEN ACCESS RECEIVED promising features as compared to electronic processors [27][28][29][30]. For instance, nanosecond-scale routing and reconfigurability have already been demonstrated [31][32][33], while encoding information in photons enables decision-making at the speed of light, only limited by the generation and detection rates. Moreover, the use of phase-change materials for in-memory information processing [34] promises to enhance the energy efficiency, since their properties can be modified without continuous external intervention [35,36]. Importantly, since the architecture uses single photons, decision-making is fueled by genuine quantum randomness. This feature marks a fundamental departure from pseudorandom number generation in conventional devices. (ii) The second contribution is the development of a specific variant of PS based on binary decision trees (tree-PS, or t-PS for short), which is closely connected to the standard PS and suitable for the implementation on a photonic circuit. Furthermore, we discuss how this variant enables key features of artificial intelligence, namely abstraction and generalization [37,38].The Article is structured as follows. In section 2 we summarize the theoretical framework of RL, exemplified by three common approaches: SARSA, Q-learning, and PS. In section 3 we describe the blueprint for a fully integrated, photonic RL agent. We then numerically investigate its performance within two standard RL tasks and under realistic experimental imperfections in section 4. Finally, in section 5 we discuss promising features of this architecture within the context of t-PS.

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All-optical neural network with nonlinear activation functions

Cited by 336 publications

References 49 publications

A Review of Optical Neural Networks

A Review of Optical Neural Networks

Deep Learning Enabled Nanophotonics

Photonic architecture for reinforcement learning

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