Tensor analytics lays the mathematical basis for the prosperous promotion of multiway signal processing. To increase computing throughput, mainstream processors transform tensor convolutions into ...matrix multiplications to enhance the parallelism of computing. However, such order-reducing transformation produces data duplicates and consumes additional memory. Here, we propose an integrated photonic tensor flow processor (PTFP) without digitally duplicating the input data. It outputs the convolved tensor as the input tensor 'flows' through the processor. The hybrid manipulation of optical wavelengths, space dimensions, and time delay steps, enables the direct representation and processing of high-order tensors in the optical domain. In the proof-of-concept experiment, an integrated processor manipulating wavelengths and delay steps is implemented for demonstrating the key functionalities of PTFP. The multi-channel images and videos are processed at the modulation rate of 20 Gbaud. A convolutional neural network for video action recognition is demonstrated on the processor, which achieves an accuracy of 97.9%.
This paper presents a novel method that realizes simultaneous and completely discriminative measurement of strain and temperature using one piece of Panda-type polarization-maintaining fibre. Two ...independent optical parameters in the fiber, the Brillouin frequency shift and the birefringence, are measured by evaluating the spectrum of stimulated Brillouin scattering (SBS) and that of the dynamic acoustic grating generated in SBS to get two independent responses to strain and temperature. We found that the Brillouin frequency shift and the birefringence have the same signs for strain-dependence but opposite signs for temperature-dependence. In experiment, the birefringence in the PMF is characterized with a precision of approximately 10(-8) by detecting the diffraction spectrum of the dynamic acoustic grating. A reproducible accuracy of discriminating strain and temperature as fine as 3 micro-strains and 0.08 degrees Celsius is demonstrated.
This paper proposes a novel reflectometry based on the frequency modulation pulse-compression technology, called optical pulse compression reflectometry (OPCR). Linear frequency modulation (LFM) ...pulse is taken as an example to implement the OPCR. Its working principle and theoretical analysis are demonstrated. The spatial resolution is determined by the sweeping range of the LFM rather than the pulse width, which overcomes the tradeoff between spatial resolution and measurement range in the conventional pulse-based optical time domain reflectometry. The influence of the laser's phase noise on the integrated side lobe ratio and peak side lobe ratio is theoretically studied. Thanks to the continuous acquisition nature of the OPCR, time averaging is valid to eliminate the influence and results in the measurement range of the OPCR beyond a few times of the source coherent length. A proof-of-concept experiment of the OPCR is carried out to verify the spatial resolution and measurement range.
Abstract
Optical implementations of neural networks (ONNs) herald the next-generation high-speed and energy-efficient deep learning computing by harnessing the technical advantages of large bandwidth ...and high parallelism of optics. However, due to the problems of the incomplete numerical domain, limited hardware scale, or inadequate numerical accuracy, the majority of existing ONNs were studied for basic classification tasks. Given that regression is a fundamental form of deep learning and accounts for a large part of current artificial intelligence applications, it is necessary to master deep learning regression for further development and deployment of ONNs. Here, we demonstrate a silicon-based optical coherent dot-product chip (OCDC) capable of completing deep learning regression tasks. The OCDC adopts optical fields to carry out operations in the complete real-value domain instead of in only the positive domain. Via reusing, a single chip conducts matrix multiplications and convolutions in neural networks of any complexity. Also, hardware deviations are compensated via in-situ backpropagation control provided the simplicity of chip architecture. Therefore, the OCDC meets the requirements for sophisticated regression tasks and we successfully demonstrate a representative neural network, the AUTOMAP (a cutting-edge neural network model for image reconstruction). The quality of reconstructed images by the OCDC and a 32-bit digital computer is comparable. To the best of our knowledge, there is no precedent of performing such state-of-the-art regression tasks on ONN chips. It is anticipated that the OCDC can promote the novel accomplishment of ONNs in modern AI applications including autonomous driving, natural language processing, and scientific study.
We propose a Brillouin optical correlation-domain reflectometry (BOCDR), which can measure the distribution of strain and/or temperature along an optical fiber from a single end, by detecting ...spontaneous Brillouin scattering with controlling the interference of continuous lightwaves. In a pulse-based conventional Brillouin optical time-domain reflectometry (BOTDR), it is difficult in principle to achieve a spatial resolution less than 1 m, and the measurement time is as long as 5-10 minutes. On the contrary, the continuous-wave-based BOCDR can exceed the limit of 1-m resolution, and realize much faster measurement and random access to measuring positions. Spatial resolution of 40 cm was experimentally demonstrated with sampling rate of 50 Hz.
In this paper, a Blockchain-driven platform for supply chain finance, BCautoSCF (Zhi-lian-che-rong in Chinese), is introduced. It is successfully established as a reliable and efficient financing ...platform for the auto retail industry. Due to the Blockchain built-in trust mechanism, participants in the supply chain (SC) networks work extensively and transparently to run a reliable, convenient, and traceable business. Likewise, the traditional supply chain finance (SCF), partial automation of SCF workflows with fewer human errors and disruptions was achieved through smart contract in BCautoSCF. Such open and secure features suggest the feasibility of BCautoSCF in SCF. As the first Blockchain-driven SCF application for the auto retail industry in China, our contribution lies in studying these pain points existing in traditional SCF and proposing a novel Blockchain-driven design to reshape the business logic of SCF to develop an efficient and reliable financing platform for small and medium enterprises (SMEs) in the auto retail industry to decrease the cost of financing and speed up the cash flows. Currently, there are over 600 active enterprise users that adopt BCautoSCF to run their financing business. Up to October 2019, the BCautoSCF provides services to 449 online/offline auto retailors, three B2B asset exchange platforms, nine fund providers, and 78 logistic services across 21 provinces in China. There are 3296 financing transactions successfully completed in BCautoSCF, and the amount of financing is ¥566,784,802.18. In the future, we will work towards supporting a full automation of SCF workflow by smart contracts, so that the efficiency of transaction will be further improved.
The emergence of parallel convolution-operation technology has substantially powered the complexity and functionality of optical neural networks (ONN) by harnessing the dimension of optical ...wavelength. However, this advanced architecture faces remarkable challenges in high-level integration and on-chip operation. In this work, convolution based on time-wavelength plane stretching approach is implemented on a microcomb-driven chip-based photonic processing unit (PPU). To support the operation of this processing unit, we develop a dedicated control and operation protocol, leading to a record high weight precision of 9 bits. Moreover, the compact architecture and high data loading speed enable a preeminent photonic-core compute density of over 1 trillion of operations per second per square millimeter (TOPS mm
). Two proof-of-concept experiments are demonstrated, including image edge detection and handwritten digit recognition, showing comparable processing capability compared to that of a digital computer. Due to the advanced performance and the great scalability, this parallel photonic processing unit can potentially revolutionize sophisticated artificial intelligence tasks including autonomous driving, video action recognition and image reconstruction.
The discovery of two-dimensional (2D) material graphene has opened a door towards a class of layered novel nanomaterials with unique photonic and optoelectronic properties. Recently many new 2D ...materials have been reported, including topological insulators (TIs), transition metal dichalcogenides (TMDs) and black phosphorus (BP). These materials have been demonstrated high optical nonlinearity and Pauli blocking used as saturable absorbers (SAs) in pulsed lasers. In this paper, we summarize the current specifications from these 2D materials based mode-locked and Q-switched lasers. The laser performance in operating wavelength, optical bandwidth, repetition rate and pulse energy is reviewed. Finally future perspective is suggested.
•Mode-locked and Q-switched lasers based on three 2D materials are reviewed.•Best specifications of bandwidth, repetition rate and pulse energy are summarized.•Material basic properties, fabrication and characterization are also introduced.
Brain-inspired photonic neural networks for artificial intelligence have attracted renewed interest. For many computational tasks, such as image recognition, speech processing and deep learning, ...photonic neural networks have the potential to increase the computing speed and energy efficiency on the orders of magnitude compared with digital electronics. Silicon Photonics, which combines the advantages of electronics and photonics, brings hope for the large-scale photonic neural network integration. This paper walks through the basic concept of artificial neural networks and focuses on the key devices which construct the silicon photonic neuromorphic systems. We review some recent important progress in silicon photonic neural networks, which include multilayer artificial neural networks and brain-like neuromorphic systems, for artificial intelligence. A prototype of silicon photonic artificial intelligence processor for ultra-fast neural network computing is also proposed. We hope this paper gives a detailed overview and a deeper understanding of this emerging field.
In the process of stimulated Brillouin scattering (SBS), acoustic waves are generated by the interference of counterpropagating optical waves via electrostriction effect. These acoustic waves not ...only stimulate the Brillouin scattering process, but can also play a role of moving Bragg reflector for another optical wave when a proper phase-matching condition is satisfied between the interacting optical waves. Brillouin dynamic grating (BDG) represents this secondary role of the acoustic wave, which shows a unique reflection spectrum, called BDG spectrum. The BDG spectrum generally shows higher sensitivity to the change of physical variables, such as temperature and strain than ordinary Brillouin gain spectrum (BGS), and also provides information on the waveguide parameters, such as polarization or modal birefringence with high accuracy. This paper reviews the operation principle of BDG under various conditions and the research progresses on its application to distributed fiber sensors.
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