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Register a new userMode-Division Multiplexing for Silicon Photonic Network-on-chip
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Added by
Xinru Wu Wu Xinru
Publication date
2017
fields
Informatics Engineering
and research's language is
English
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Optical interconnect is a potential solution to attain the large bandwidth on-chip communications needed in high performance computers in a low power and low cost manner. Mode-division multiplexing (MDM) is an emerging technology that scales the capacity of a single wavelength carrier by the number of modes in a multimode waveguide, and is attractive as a cost-effective means for high bandwidth density on-chip communications. Advanced modulation formats with high spectral efficiency in MDM networks can further improve the data rates of the optical link. Here, we demonstrate an intra-chip MDM communications link employing advanced modulation formats with two waveguide modes. We demonstrate a compact single wavelength carrier link that is expected to support 2x100 Gb/s mode multiplexed capacity. The network comprised integrated microring modulators at the transmitter, mode multiplexers, multimode waveguide interconnect, mode demultiplexers and integrated germanium on silicon photodetectors. Each of the mode channels achieves 100 Gb/s line rate with 84 Gb/s net payload data rate at 7% overhead for hard-decision forward error correction (HD-FEC) in the OFDM/16-QAM signal transmission.
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Reinforcement learning involves decision making in dynamic and uncertain environments and constitutes a crucial element of artificial intelligence. In our previous work, we experimentally demonstrated that the ultrafast chaotic oscillatory dynamics of lasers can be used to solve the two-armed bandit problem efficiently, which requires decision making concerning a class of difficult trade-offs called the exploration-exploitation dilemma. However, only two selections were employed in that research; thus, the scalability of the laser-chaos-based reinforcement learning should be clarified. In this study, we demonstrated a scalable, pipelined principle of resolving the multi-armed bandit problem by introducing time-division multiplexing of chaotically oscillated ultrafast time-series. The experimental demonstrations in which bandit problems with up to 64 arms were successfully solved are presented in this report. Detailed analyses are also provided that include performance comparisons among laser chaos signals generated in different physical conditions, which coincide with the diffusivity inherent in the time series. This study paves the way for ultrafast reinforcement learning by taking advantage of the ultrahigh bandwidths of light wave and practical enabling technologies.
Deep neural networks with applications from computer vision and image processing to medical diagnosis are commonly implemented using clock-based processors, where computation speed is limited by the clock frequency and the memory access time. Advances in photonic integrated circuits have enabled research in photonic computation, where, despite excellent features such as fast linear computation, no integrated photonic deep network has been demonstrated to date due to the lack of scalable nonlinear functionality and the loss of photonic devices, making scalability to a large number of layers challenging. Here we report the first integrated end-to-end photonic deep neural network (PDNN) that performs instantaneous image classification through direct processing of optical waves. Images are formed on the input pixels and optical waves are coupled into nanophotonic waveguides and processed as the light propagates through layers of neurons on-chip. Each neuron generates an optical output from input optical signals, where linear computation is performed optically and the nonlinear activation function is realised opto-electronically. The output of a laser coupled into the chip is uniformly distributed among all neurons within the network providing the same per-neuron supply light. Thus, all neurons have the same optical output range enabling scalability to deep networks with large number of layers. The PDNN chip is used for 2- and 4-class classification of handwritten letters achieving accuracies of higher than 93.7% and 90.3%, respectively, with a computation time less than one clock cycle of state-of-the-art digital computation platforms. Direct clock-less processing of optical data eliminates photo-detection, A/D conversion, and the requirement for a large memory module, enabling significantly faster and more energy-efficient neural networks for the next generations of deep learning systems.
We experimentally demonstrate a record net capacity per wavelength of 1.23~Tb/s over a single silicon-on-insulator (SOI) multimode waveguide for optical interconnects employing on-chip mode-division multiplexing and 11$times$11 multiple-in-multiple-out (MIMO) digital signal processing.
With the advent of multi-core processors, network-on-chip design has been key in addressing network performances, such as bandwidth, power consumption, and communication delays when dealing with on-chip communication between the increasing number of processor cores. As the numbers of cores increase, network design becomes more complex. Therefore, there is a critical need in soliciting computer aid in determining network configurations that afford optimal performance given resources and design constraints. We propose a Pareto-optimization framework that explores the space of possible network configurations to determine optimal network latencies, power consumption, and the corresponding link allocations. For a given number of routers, average network latency and power consumption as example performance objectives can be displayed in form of Pareto-optimal fronts, thus not only offering a design tool, but also enabling trade-off studies.
The development of mode-division multiplexing techniques is an important step to increase the information processing capacity. In this context, we design an efficient and robust mode-division (de)multiplexing integrated device based on the combination of spatial adiabatic passage and supersymmetric techniques. It consists of two identical step-index external waveguides coupled to a supersymmetric central one with a specific modal content that prevents the transfer of the fundamental transverse electric spatial mode. The separation between waveguides is engineered along the propagation direction to optimize spatial adiabatic passage for the first excited transverse electric spatial mode of the step-index waveguides. Thus, by injecting a superposition of the two lowest spatial modes into the step-index left waveguide, the fundamental mode remains in the left waveguide while the first excited mode is fully transmitted to the right waveguide. Output fidelities $mathcal{F}>0.90$ are obtained for a broad range of geometrical parameter values and lights wavelengths, reaching $mathcal{F}=0.99$ for optimized values.
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