Search

Space-division multiplexing is a widely used technique to improve data-carrying capacities in both wireless and optical communication systems. However, tightly packed spatial channels cause severe crosstalk. High data rates and large channel counts impose severe constraints on resolving the crosstalk using traditional digital signal processing algorithms and electronic circuits. In order to solve these issues, this paper presents a silicon photonic system combining high-speed silicon photonic devices with a novel blind source separation (BSS) algorithm. We first demonstrate using photonic BSS to undo modal crosstalk in a short-reach multimode optical fiber interconnect for intra-data-center communications. The proposed photonic BSS system inherits the advantages of photonic matrix processor and the “blindness” of BSS, leading to superior energy and cost efficiency and reduced latency, while allowing to recover the signals using a sub-Nyquist sampling rate and in a free-running mode, and offering unmatched agility in signal format and data rate. The feasibility of using photonic processors for mode crosstalk equalization has been recently demonstrated, assisted with training sequences. Our approach, photonic BSS, in contrast, can tackle the more difficult problem of making the receiver transparent to any data rate and modulation format, and workable with slow and cost-effective electronics. In addition, we find that photonic BSS has a much better scaling law for space-division multiplexing (SDM)-based communication systems than digital signal processing (DSP). When compared to state-of-the-art DSP, photonic BSS can reduce system power consumption, speed, and latency by several orders of magnitude, particularly for high-capacity communications with high data rates per channel and a large number of channels. Photonic BSS has the added advantages of being agnostic to transmission content, making it exceptional at protecting communication privacy. This paper also discusses our previous work in demonstrating photonic BSS for privacy protection in wireless multiple-in multiple-out (MIMO) communications using silicon photonic micoring resonator (MRR) weight banks. [ABSTRACT FROM AUTHOR]

Spiking neural networks enable efficient information processing in real-time. Excitable lasers can exhibit ultrafast spiking dynamics, and when preceded by a photodetector in an O/E/O link, can process optical spikes at different wavelengths and thus be interconnected in large neural networks. Here, we experimentally demonstrate and numerically simulate the spiking dynamics of a laser neuron fabricated in a photonic integrated circuit. Our spiking laser neuron is shown to perform coincidence detection with nanosecond time resolution, and we observe refractory periods in the order of 0.1 ns. We propose a method to implement XOR classification using our laser neurons, and simulations of the resultant dynamics indicate robust tolerance to timing jitter. [ABSTRACT FROM AUTHOR]

Convolutional Neural Networks (CNNs) are powerful and highly ubiquitous tools for extracting features from large datasets for applications such as computer vision and natural language processing. However, a convolution is a computationally expensive operation in digital electronics. In contrast, neuromorphic photonic systems, which have experienced a recent surge of interest over the last few years, propose higher bandwidth and energy efficiencies for neural network training and inference. Neuromorphic photonics exploits the advantages of optical electronics, including the ease of analog processing, and busing multiple signals on a single waveguide at the speed of light. Here, we propose a Digital Electronic and Analog Photonic (DEAP) CNN hardware architecture that has potential to be 2.8 to 14 times faster while using almost 25% less energy than current state-of-the-art graphical processing units (GPUs). [ABSTRACT FROM AUTHOR]

It has long been known that photonic communication can alleviate the data movement bottlenecks that plague conventional microelectronic processors. More recently, there has also been interest in its capabilities to implement low precision linear operations, such as matrix multiplications, fast and efficiently. We characterize the performance of photonic and electronic hardware underlying neural network models using multiply-accumulate operations. First, we investigate the limits of analog electronic crossbar arrays and on-chip photonic linear computing systems. Photonic processors are shown to have advantages in the limit of large processor sizes (${>}\text{100}\; \mu$ m), large vector sizes ($N > 500)$ , and low noise precision (${\leq} 4$ bits). We discuss several proposed tunable photonic MAC systems, and provide a concrete comparison between deep learning and photonic hardware using several empirically-validated device and system models. We show significant potential improvements over digital electronics in energy (${>}10^2$), speed (${>}10^3$), and compute density (${>}10^2$). [ABSTRACT FROM AUTHOR]

Neuromorphic photonics relies on efficiently emulating analog neural networks at high speeds. Prior work showed that transducing signals from the optical to the electrical domain and back with transimpedance gain was an efficient approach to implementing analog photonic neurons and scalable networks. Here, we examine modulator-based photonic neuron circuits with passive and active transimpedance gains, with special attention to the sources of noise propagation. We find that a modulator nonlinear transfer function can suppress noise, which is necessary to avoid noise propagation in hardware neural networks. In addition, while efficient modulators can reduce power for an individual neuron, signal-to-noise ratios must be traded off with power consumption at a system level. Active transimpedance amplifiers may help relax this tradeoff for conventional p-n junction silicon photonic modulators, but a passive transimpedance circuit is sufficient when very efficient modulators (i.e., low C and low V-pi) are employed. [ABSTRACT FROM AUTHOR]

Multi-antenna radio front-ends generate a multi-dimensional flood of information, most of which is partially redundant. Redundancy is eliminated by dimensionality reduction. This class of operations presents harsh fundamental tradeoffs to contemporary digital processing techniques. These tradeoffs can be broken in the analog domain, in which the performance of optical technologies greatly exceeds that of electronic counterparts. Here, we present concepts, methods, and a first demonstration of multivariate photonics: a combination of integrated photonic hardware, analog dimensionality reduction, and blind algorithmic techniques. We experimentally demonstrate 2-channel, 1.0 GHz principal component analysis in a photonic weight bank using recently proposed algorithms for synthesizing the multivariate properties of signals to which the receiver is blind. Novel methods are introduced for controlling blindness conditions in a laboratory context. This work provides a foundation for further research in multivariate photonic information processing, which is poised to play a role in future generations of wireless technology. [ABSTRACT FROM AUTHOR]

We experimentally demonstrate an all-optical programmable thresholder on a silicon photonic circuit. By exploiting the nonlinearities in a resonator-assisted Mach-Zehnder interferometer (MZI), the proposed optical thresholder can discriminate two optical signals with a power contract ratio as low as 1.13. We experimentally achieve a signal contrast enhancement of 40, which leads to a bit error rate (BER) improvement by 5 orders of magnitude and a receiver sensitivity improvement of 11 dB. We present the thresholding function of our device and validate the function with experimental data. Furthermore, we investigate potential device speed improvement by reducing the carrier lifetime. [ABSTRACT FROM AUTHOR]

Neuromorphic photonics has experienced a recent surge of interest over the last few years, promising orders of magnitude improvements in both speed and energy efficiency over digital electronics. This paper provides a tutorial overview of neuromorphic photonic systems and their application to optimization and machine learning problems. We discuss the physical advantages of photonic processing systems, and we describe underlying device models that allow practical systems to be constructed. We also describe several real-world applications for control and deep learning inference. Finally, we discuss scalability in the context of designing a full-scale neuromorphic photonic processing system, considering aspects such as signal integrity, noise, and hardware fabrication platforms. The paper is intended for a wide audience and teaches how theory, research, and device concepts from neuromorphic photonics could be applied in practical machine learning systems. [ABSTRACT FROM AUTHOR]

We are witnessing a rising concern over communication security and privacy. Conventional cryptography techniques encrypt data into unreadable codes, but still expose the existence of communications through metadata information (e.g., packet timing and size). In contrast, we propose a steganographic communication scheme adopting a novel combination of the intrinsic optical noise and a unique signal spreading technique to hide the existence of optical communications, i.e., optical steganography. We experimentally implement a prototype steganographic communication system, which requires zero cover-signal overhead to enable a stealth communication channel over the existing communication infrastructure. Both the frequency and time-domain characterizations of our prototype implementation verify the feasibility of our approach. We also demonstrate the first practical steganographic communication that provides reliable communication performances between real computers at the application layer (200–300 Mb/s file transfer rate and 25–30 Mb/s Internet data rate) over long-distance optic fibers (25–50 km). Additional bit-error-rate measurements illustrate negligible channel interference between the public and stealth communications (less than 1 dB power penalty). We further quantitatively demonstrate that the eavesdropper's chance of matching system parameters to effectively recover the stealth signal is ${2^{-10}}$ by random guessing, and that the eavesdropper's ability of detecting the stealth signal hidden in the transmission channel is strictly limited close to a random guess. Our steganographic communication scheme provides an attractive foundation for mitigating eavesdropping at the link level; thus, paving the way for future privacy-enhancing technologies using the physical layer characteristics of communication links. [ABSTRACT FROM AUTHOR]

We demonstrate a passively $Q$ -switched, dual-band Tm3+:ZBLAN fiber laser operating simultaneously around 1895 and 2315 nm using bidirectional pumping at 795 nm and a graphene saturable absorber. The Q$ -switched pulse trains at 1895 and 2315 nm have a synchronized repetition rate from 10.8 to 25.2 kHz with pulse durations as short as 4.5 and 4.9~\mu \text{s} , respectively. [ABSTRACT FROM PUBLISHER]