Deep learning with coherent nanophotonic circuits
Yichen Shen,Nicholas C. Harris,Scott Skirlo,Dirk Englund,Marin Soljacic +4 more
- Vol. 11, Iss: 7, pp 441-446
TLDR
A new architecture for a fully optical neural network is demonstrated that enables a computational speed enhancement of at least two orders of magnitude and three order of magnitude in power efficiency over state-of-the-art electronics.Abstract:
Artificial Neural Networks have dramatically improved performance for many machine learning tasks. We demonstrate a new architecture for a fully optical neural network that enables a computational speed enhancement of at least two orders of magnitude and three orders of magnitude in power efficiency over state-of-the-art electronics.read more
Citations
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Journal ArticleDOI
Continuous-variable quantum neural networks
Nathan Killoran,Thomas R. Bromley,Juan Miguel Arrazola,Maria Schuld,Nicolás Quesada,Seth Lloyd +5 more
TL;DR: The quantum neural network as mentioned in this paper is a variational quantum circuit built in the continuous-variable (CV) architecture, which encodes quantum information in continuous degrees of freedom such as the amplitudes of the electromagnetic field.
Journal ArticleDOI
Recent Progress in Photonic Synapses for Neuromorphic Systems
TL;DR: A summary of the development of different kinds of emerging materials utilized in photonic synaptic devices including memristors, field‐effect transistors, and phase change memory is presented, followed by the innovative applications of photonic synapses for neuromorphic systems.
Journal ArticleDOI
In situ optical backpropagation training of diffractive optical neural networks
Tiankuang Zhou,Lu Fang,Tao Yan,Jiamin Wu,Yipeng Li,Jingtao Fan,Huaqiang Wu,Xing Lin,Qionghai Dai +8 more
TL;DR: The proposed in situ optical learning architecture achieves accuracy comparable to in silico training with an electronic computer on the tasks of object classification and matrix-vector multiplication, which further allows the diffractive optical neural network to adapt to system imperfections.
Journal ArticleDOI
Nanophotonic Inverse Design with SPINS: Software Architecture and Practical Considerations
TL;DR: The Stanford Photonic INverse design Software (SPINS) as mentioned in this paper is a design framework that emphasizes flexibility and reproducible results by factoring the inverse design process into components that can be swapped out for one another.
Journal ArticleDOI
Machine-Learning-Assisted Metasurface Design for High-Efficiency Thermal Emitter Optimization
TL;DR: The proposed approach could enable a much broader scope of the optimal designs and data-driven materials synthesis that goes beyond photonic and optoelectronic applications and could become crucial for multi-constrained problems.
References
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