Principles of Neuromorphic Photonics

In an age overrun with information, the ability to process reams of data has become crucial. The demand for data will continue to grow as smart gadgets multiply and become increasingly integrated into our daily lives. Next-generation industries in artificial intelligence services and high-performance computing are so far supported by microelectronic platforms. These data-intensive enterprises rely on continual improvements in hardware. Their prospects are running up against a stark reality: conventional one-size-fits-all solutions offered by digital electronics can no longer satisfy this need, as Moore's law (exponential hardware scaling), interconnection density, and the von Neumann architecture reach their limits. With its superior speed and reconfigurability, analog photonics can provide some relief to these problems; however, complex applications of analog photonics have remained largely unexplored due to the absence of a robust photonic integration industry. Recently, the landscape for commercially-manufacturable photonic chips has been changing rapidly and now promises to achieve economies of scale previously enjoyed solely by microelectronics. The scientific community has set out to build bridges between the domains of photonic device physics and neural networks, giving rise to the field of \emph{neuromorphic photonics}. This article reviews the recent progress in integrated neuromorphic photonics. We provide an overview of neuromorphic computing, discuss the associated technology (microelectronic and photonic) platforms and compare their metric performance. We discuss photonic neural network approaches and challenges for integrated neuromorphic photonic processors while providing an in-depth description of photonic neurons and a candidate interconnection architecture. We conclude with a future outlook of neuro-inspired photonic processing.Comment: 28 pages, 19 figure

Nervous Necrosis Virus Replicates Following the Embryo Development and Dual Infection with Iridovirus at Juvenile Stage in Grouper

Infection of virus (such as nodavirus and iridovirus) and bacteria (such as Vibrio anguillarum) in farmed grouper has been widely reported and caused large economic losses to Taiwanese fish aquaculture industry since 1979. The multiplex assay was used to detect dual viral infection and showed that only nervous necrosis virus (NNV) can be detected till the end of experiments (100% mortality) once it appeared. In addition, iridovirus can be detected in a certain period of rearing. The results of real-time PCR and in situ PCR indicated that NNV, in fact, was not on the surface of the eggs but present in the embryo, which can continue to replicate during the embryo development. The virus may be vertically transmitted by packing into eggs during egg development (formation) or delivering into eggs by sperm during fertilization. The ozone treatment of eggs may fail to remove the virus, so a new strategy to prevent NNV is needed

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Photonic neural networks for ultrafast neural information processing

Photonic neural networks (PNNs) represent an important class of optical computing with the goal of producing an accelerated processor that combines the information processing capacity of neuromorphic systems, and the speed and bandwidth of photonics. This thesis focuses on system design, experimental demonstration and AI applications of PNNs using integrated photonics. Two main thrusts of the PNNs development in this thesis are: studying bio-inspired spiking network on InP-based integrated photonic circuits, and building scalable continuous-time neural network using silicon photonics. Toward the first thrust, we study the temporal dynamics of an integrated excitable laser, and demonstrate its analogy to a biological spiking neuron and its compatibility for large-scale system integration. With a solid experimental demonstration, we further propose the model of such photonic spiking neural network, and show its applications including temporal XOR task, time series processing, and recommendation systems. For the second thrust, we investigate a silicon photonics-based system to achieve both precise weight control and programmable nonlinearity. We further explore its application to real-world problems in communication systems. The proposed compact model using silicon photonic recurrent neural network enables real-time specific emitter identification, and provides a promising platform for future edge AI systems

Statistical Correlations of the N-particle Moshinsky Model

We study the correlation of the ground state of an N-particle Moshinsky model by computing the Shannon entropy in both position and momentum spaces. We have derived the Shannon entropy and mutual information with analytical forms of such an N-particle Moshinsky model, and this helps us test the entropic uncertainty principle. The Shannon entropy in position space decreases as interaction strength increases. However, Shannon entropy in momentum space has the opposite trend. Shannon entropy of the whole system satisfies the equality of entropic uncertainty principle. Our results also indicate that, independent of the sizes of the two subsystems, the mutual information increases monotonically as the interaction strength increases