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Co-existing fixed-grid and flex-grid (i.e., mixed- grid) optical networks introduce new challenges for network orchestration. Such mixed-grid networks are often controlled by hierarchical distributed architecture comprising of Optical Network Controllers and Software-Defined Network Controllers. Optimal deployment of these controllers is very important for efficient management of mixed-grid optical networks.
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Optical Networking in Smart City and Wireless Future Networks Platforms
Innovation in optical networks is essential to delivering advanced performance for future smart city and wireless networks. Incorporating optical systems research in real-world platforms presents a number of challenges, which are addressed through recent advances in the use of software defined networking and emulation.
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- Award ID(s):
- 2029295
- PAR ID:
- 10354720
- Date Published:
- Journal Name:
- https://www.ecoc2021.org/
- Page Range / eLocation ID:
- 1 to 4
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Deep learning has become a widespread tool in both science and industry. However, continued progress is hampered by the rapid growth in energy costs of ever-larger deep neural networks. Optical neural networks provide a potential means to solve the energy-cost problem faced by deep learning. Here, we experimentally demonstrate an optical neural network based on optical dot products that achieves 99% accuracy on handwritten-digit classification using ~3.1 detected photons per weight multiplication and ~90% accuracy using ~0.66 photons (~2.5 × 10 −19 J of optical energy) per weight multiplication. The fundamental principle enabling our sub-photon-per-multiplication demonstration—noise reduction from the accumulation of scalar multiplications in dot-product sums—is applicable to many different optical-neural-network architectures. Our work shows that optical neural networks can achieve accurate results using extremely low optical energies.more » « less
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Abstract Diffractive optical neural networks have shown promising advantages over electronic circuits for accelerating modern machine learning (ML) algorithms. However, it is challenging to achieve fully programmable all‐optical implementation and rapid hardware deployment. Here, a large‐scale, cost‐effective, complex‐valued, and reconfigurable diffractive all‐optical neural networks system in the visible range is demonstrated based on cascaded transmissive twisted nematic liquid crystal spatial light modulators. The employment of categorical reparameterization technique creates a physics‐aware training framework for the fast and accurate deployment of computer‐trained models onto optical hardware. Such a full stack of hardware and software enables not only the experimental demonstration of classifying handwritten digits in standard datasets, but also theoretical analysis and experimental verification of physics‐aware adversarial attacks onto the system, which are generated from a complex‐valued gradient‐based algorithm. The detailed adversarial robustness comparison with conventional multiple layer perceptrons and convolutional neural networks features a distinct statistical adversarial property in diffractive optical neural networks. The developed full stack of software and hardware provides new opportunities of employing diffractive optics in a variety of ML tasks and in the research on optical adversarial ML.more » « less
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Modern computing and communication technologies such as supercomputers and the Internet are based on optically connected networks of microwave-frequency information processors. An analogous architecture has been proposed for quantum networks, using optical photons to distribute entanglement between remote superconducting quantum processors. Here we report a step towards such a network by observing non-classical correlations between photons in an optical link and a superconducting quantum device. We generate these states of light through a spontaneous parametric down-conversion process in a chip-scale piezo-optomechanical transducer, and we measure a microwave–optical cross-correlation exceeding the Cauchy–Schwarz classical bound for thermal states. As further evidence of the non-classical character of the microwave–optical photon pairs, we observe antibunching in the microwave state conditioned on detection of an optical photon. Such a transducer can be readily connected to an independent superconducting qubit module and serve as a key building block for optical quantum networks of microwave-frequency qubits.more » « less
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The explosive growth in computation and energy cost of artificial intelligence has spurred interest in alternative computing modalities to conventional electronic processors. Photonic processors, which use photons instead of electrons, promise optical neural networks with ultralow latency and power consumption. However, existing optical neural networks, limited by their designs, have not achieved the recognition accuracy of modern electronic neural networks. In this work, we bridge this gap by embedding parallelized optical computation into flat camera optics that perform neural network computations during capture, before recording on the sensor. We leverage large kernels and propose a spatially varying convolutional network learned through a low-dimensional reparameterization. We instantiate this network inside the camera lens with a nanophotonic array with angle-dependent responses. Combined with a lightweight electronic back-end of about 2K parameters, our reconfigurable nanophotonic neural network achieves 72.76% accuracy on CIFAR-10, surpassing AlexNet (72.64%), and advancing optical neural networks into the deep learning era.more » « less
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/ECOC52684.2021.9605836
