Nonvolatile PCM-driven photonic computing using programmable sub-wavelength digital metasurfaces.

Silicon photonics (SiPh) technology has facilitated the deployment of integrated photonics across different application domains, from ultra-fast communication in Datacom applications to energy-efficient optical computation in emerging hardware accelerators for machine learning. More recently, the integration of SiPh and phase change materials has created a unique opportunity to realize adaptable, reconfigurable, and programmable photonic platforms. In particular, the nonvolatile programmability in phase change materials has made them a promising candidate for implementing photonic memory cells and architectures. Accordingly, photonic memory systems and even in-memory photonic computing paradigms are on the rise, especially given their potential for improving data access in electronic and photonic processors. However, there are still many challenges in the design and fabrication of phase-change photonic integrated circuits, which need to be addressed. This article presents a comprehensive survey on the recent advances and challenges for the integration of phase change materials with contemporary photonic devices while focusing on the photonic memory application. In particular, we explore phase-change photonic memory from the material level to the architecture level by presenting an overview of different material-level characteristics of phase change materials with their optical, electrical, and thermal properties as well as their integration into SiPh devices and photonic memory architectures and their application for in-memory photonic computing. We also present a comparison with electronic memory and discuss open research challenges that must be addressed to further advance phase-change photonic memory towards successful integration into emerging computing systems.

Energy performance of phase change materials integrated into brick masonry walls for cooling load management in residential buildings

While numerous artificial neural network (ANN) models have been electronically implemented and simulated by conventional computers, optical technology provides a far superior mechanism for the implementation of large-scale ANNs. The properties of light make it an ideal carrier of data signals. With optics, very large and high speed neural network architectures are possible. Because light is a predictable phenomenon, it can be described mathematically and its behavior can be simulated by conventional computers. A hologram is in essence a capture of the light field at a particular moment in time and space. Later, the hologram can be used to reconstruct the three dimensional light field carrying optical data. This makes a hologram an ideal medium for capturing, storing, and transmitting data in optical computers, such as optical neural networks (ONNs). Holograms can be created using conventional methods, but they can also be computer generated. In this paper, we will present an overview of optical neural networks, with emphasis on the holographic neural networks. We will take a look at the mathematical basis of holography in terms of the Fresnel Zone Plate and how it can be utilized in making computer generated holograms (CGHs). Finally, we will present various methods of CGH implementation in a two layer holographic ONN.

Artificial intelligence has prevailed in all trades and professions due to the assistance of big data resources, advanced algorithms, and high-performance electronic hardware. However, conventional computing hardware is inefficient at implementing complex tasks, in large part because the memory and processor in its computing architecture are separated, performing insufficiently in computing speed and energy consumption. In recent years, optical neural networks (ONNs) have made a range of research progress in optical computing due to advantages such as sub-nanosecond latency, low heat dissipation, and high parallelism. ONNs are in prospect to provide support regarding computing speed and energy consumption for the further development of artificial intelligence with a novel computing paradigm. Herein, we first introduce the design method and principle of ONNs based on various optical elements. Then, we successively review the non-integrated ONNs consisting of volume optical components and the integrated ONNs composed of on-chip components. Finally, we summarize and discuss the computational density, nonlinearity, scalability, and practical applications of ONNs, and comment on the challenges and perspectives of the ONNs in the future development trends.

The rise of artificial intelligence (AI) has driven increasingly stringent requirements for computing speed and energy efficiency. Optical computing offers a promising alternative due to its inherent parallelism and low power consumption. Optical neural networks (ONNs), as an actively studied application of optical computing in AI, have shown potential in matrix operations and neural network inference. However, existing ONNs remain constrained by limited diversity of network layers and an insufficient number of tunable weights. Here, we propose the PCM-MD (phase-change-materials-microring-diffraction) hybrid ONN architecture based on microring resonators, diffraction slots, and phase-change materials that enables diverse optical parameterized layers. The architecture features 679 tunable weights distributed across convolutional, nonlinear, compression, and fully connected layers. PCM-MD demonstrates its effectiveness across multiple intelligent tasks, achieving 85.12% accuracy on Fashion-MNIST classification, 100% accuracy on 3-bit arithmetic addition, and enabling content generation by serving as both generator and discriminator in optical generative adversarial networks (GANs). This optical parameterized architecture provides a promising foundation for energy-efficient AI systems.

Event Abstract Back to Event Logarithmic Hybrid Optical Neural Network-type systems: towards digital-optical cognitive models of robust object recognition Ioannis Kypraios1* 1 Oxford University, Centre for Innovation & Enterprise, APEM Computing Lab, United Kingdom Abstract - By combining the complex logarithmic r-theta mapping of a space-variant imaging sensor [1, 2] with the hybrid digital-optical neural network filter together with a window unit multiple objects of the same class or of different classes can robustly be recognized. The resulted logarithmic r-theta mapping for hybrid digital-optical neural network system or briefly referred to as logarithmic hybrid optical neural network system is shown to exhibit with a single pass over the input data simultaneously in-plane rotation, out-of-plane rotation, scale, log r-θ map translation and shift invariance, and good clutter tolerance by recognizing correctly the different objects within the cluttered scenes. Here, we study the biologically-inspired knowledge learning and knowledge representation [3] achieved by the logarithmic hybrid optical neural network-type of object recognition systems (see Fig. 1 and Fig. 2). We investigate the effects that altering the knowledge representation can have on the problem’s learned knowledge and the problem solving process, in overall. Further, we study the logarithmic unconstrained-, logarithmic constrained-, and logarithmic modified-hybrid optical neural network systems architectures’ designs [4, 5]. We show how the logarithmic unconstrained-hybrid optical neural network object recognition system applies an unconstrained representation of the problem’s knowledge to maximize the search of solutions, how the logarithmic constrained-hybrid optical neural network object recognition system uses a constrained representation of the problem’s knowledge to guide the search towards certain solutions over others in the multidimensional search space, and how the logarithmic modified-hybrid optical neural network object recognition system uses memory-like masks to recall certain solutions over others in the multidimensional search space. Fig. 1. (a) Simplified human retina and visual cortex model used for description purposes only; (b) A digital-optical computational model for the cognitive interaction between the retina and the human visual cortex with the general logarithmic hybrid optical neural network architercture. Fig. 2. Biologically-inspired knowledge learning and knowledge representation with the logarithmic hybrid optical neural network-type of object recognition systems. Figure 1 Figure 2 References [1] C. F. R. Weiman, “Video compression via log-polar mapping”, Real-Time Image Processing II, SPIE Symposium on OE/Aerospace Sensing, Vol. 1295, 266-277 (1990) [2] C. G. Ho et al., “Sensor geometry and sampling methods for space-variant image processing”, Pattern Analysis and Applications, Vol. 5, 369-384 (2002) [3] I. Lee, and B. Portier, "An empirical study of knowledge representation and learning within conceptual spaces for intelligent agents", 6th IEEE/ACIS International Conference Computer and Information Science, 463-468, Melbourne, Australia (2007) [4] I. Kypraios et al., “Fully invariant complex Logarithmic r- map for the Hybrid Optical Neural Network filter for object recognition within cluttered scenes”, 50th Anniversary International Symposium ELMAR 2008, IEEE Region 8/IEEE Croatia Section/EURASIP, Zadar-Croatia, Vol. 1, 141-146 (2008) [5] I. Kypraios et al., “Logarithmic r-θ mapping for hybrid optical neural network filter for object recogntion within cluttered scenes”, Recent Advances in Multimedia Signal Processing and Communications, SCI231, Springer-Verlag Berlin Heidelberg, 91-120 (2009) Keywords: digital-optical, cognitive, object recognition, Robust and Adaptive Systems, Knowledge representation, learning and memory Conference: Neuroinformatics 2013, Stockholm, Sweden, 27 Aug - 29 Aug, 2013. Presentation Type: Poster Topic: Neuromorphic engineering Citation: Kypraios I (2013). Logarithmic Hybrid Optical Neural Network-type systems: towards digital-optical cognitive models of robust object recognition. Front. Neuroinform. Conference Abstract: Neuroinformatics 2013. doi: 10.3389/conf.fninf.2013.09.00066 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 09 Apr 2013; Published Online: 11 Jul 2013. * Correspondence: Dr. Ioannis Kypraios, Oxford University, Centre for Innovation & Enterprise, APEM Computing Lab, Begbroke, Oxfordshire, OX5 1PF, United Kingdom, ioanniskyp@yahoo.com Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Ioannis Kypraios Google Ioannis Kypraios Google Scholar Ioannis Kypraios PubMed Ioannis Kypraios Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

Optical neuromorphic computing has demonstrated promising performance with ultra-high computation speed, high bandwidth, and low energy consumption. The traditional optical neural network (ONN) architectures realize neuromorphic computing via electrical weight encoding. However, previous ONN design methodologies can only handle static linear projection with stationary synaptic weights, thus fail to support efficient and flexible computing when both operands are dynamically-encoded light signals. In this work, we propose a novel ONN engine O2NN based on wavelength-division multiplexing and differential detection to enable high-performance, robust, and versatile photonic neural computing with both light operands. Balanced optical weights and augmented quantization are introduced to enhance the representability and efficiency of our architecture. Static and dynamic variations are discussed in detail with a knowledge-distillation-based solution given for robustness improvement. Discussions on hardware cost and efficiency are provided for a comprehensive comparison with prior work. Simulation and experimental results show that the proposed ONN architecture provides flexible, efficient, and robust support for high-performance photonic neural computing with fully-optical operands under low-bit quantization and practical variations.

Optoelectronic realization of adaptive filters and equalizers using fiber optic tapped delay lines and spatial light modulators has been discussed recently. We describe the design of a single layer fiber optic Adaline neural network which can be used as a bit pattern classifier. In our realization we employ as few electronic devices as possible and use optical computation to utilize the advantages of optics in processing speed, parallelism, and interconnection. The new optical neural network described in this paper is designed for optical processing of guided lightwave signals, not electronic signals. We analyzed the convergence or learning characteristics of the optically implemented Adaline in the presence of errors in the hardware, and we studied methods for improving the convergence rate of the Adaline.© (1993) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Phase change materials (PCMs) show a good capability in absorbing massive heat when undergoing phase change, which have great potential to be incorporated into building envelopes to enhance indoor thermal comfort by preventing heat penetration into buildings and reducing energy requirements. In this work, a deep analysis of PCM enhanced-walls model has been conducted in six representative climate regions of Morocco: El Jadida, Fez, Marrakesh, Ifrane, and Errachidia. More in detail, numerical simulations were carried out to assess the thermal behavior and energy performance of a residential building integrated with four different PCMs. The results showed that the effectiveness and selection of PCMs strongly depend on local weather where they are applied, characteristics of HVAC systems, PCM layer thickness, and position. Furthermore, with reference to each climate zone, the appropriate PCM leading to the lowest annual energy consumption was identified. The findings show that PCMs are particularly suitable for Mediterranean climates, which a promising annual energy saving of about 41% was obtained. While, the lowest value was recorded in Errachidia city reveals that the integration of PCM has little effect in desert climate zone. As for the other climates considered, values of about 28% to 31% were achieved in the studied house model.

This article investigates the importance of the design parameters when looking at possible energy savings and comfort enhancement in a building using phase change materials (PCMs). Computer-based simulations are performed using a simulation software for modelling a house and its thermal behaviour over a year. It is found that by varying the heating set point and the phase-change (melting) temperature range of the PCM, significant changes can be observed. Some poor scenarios show that the integration of PCM can increase both the discomfort (up to 6% more discomfort hours) and the energy requirements (up to 25% more energy needed). On the other hand, appropriate scenarios bring significant energy savings (up to 33% less energy needed) and comfort enhancement (up to 31% less discomfort hours). This highlights the strong need for a clever design when integrating PCM into buildings. The goal is to find a trade-off between energy savings and comfort enhancement. The PCM with a phase-change temperature range between 21°C and 26°C shows the best results. The study is based on climate conditions for Auckland City in New Zealand, but most of the conclusions drawn can be applied to any climate.

The massive interconnection and parallel operation in an optical neural network (ONN) require a high-resolution, large-dynamic-range spatial light modulator (SLM) for the construction of the interconnectionweight matrix (IWM). However, the resolution of currently available SLM's is limited, and this is a major obstacle in the development of a large-scale optical neural computers. We use a space-time-sharing technique to improve the resolution of an optical neural operation. Because the ONN we recently developed1 uses incoherent light, the space–bandwidth product of the network can be expanded by space-time multiplexing. In other words, a large-scale neural operation can be accomplished with a smaller network, by trading off the processing speed. To accomplish this task, we have shown that a large-scale IWM can be partitioned into arrays of sub-IWM's, of which the space–bandwidth product of the sub-IWM's is equal to the number of neurons in the neural network. Then by sequentially displaying these arrays of sub-IWM's, a larger (nfold) space–bandwidth image pattern can be obtained by using a smaller ONN. Computer-simulated results and experimental demonstrations are also provided in this paper.

Transient CFD Analysis of Macro-Encapsulated Latent Heat Thermal Energy Storage Containers Incorporated within Solar Air Heater

The growing computational demands of artificial intelligence motivate the development of optical neural networks (ONNs), while their practical deployment remains constrained by optimization complexity and limited adaptability. We propose a mutual learning knowledge distillation framework for optical diffractive neural networks (ODNNs), synergizing photonic computing with adaptive training strategies. Our architecture enables bidirectional knowledge transfer between parameter-efficient optical student networks and electrical ResNet teachers, leveraging both labeled data and probabilistic guidance. The validation on Fashion-MNIST and Kuzushiji-MNIST demonstrates classification accuracy improvements of 5.9% and 13.88%, respectively, over conventional ODNNs training. In addition, we further developed the photonic to photonic mutual learning computational paradigm, in which an optical network with 0.98 × 106 parameters can bootstrap compact photonic classifiers with 0.19 × 106 parameters, achieving an accuracy of 79.5% on MNIST and yielding an absolute improvement of 9% relative to the noncooperative baseline. Even though the all-optical mutual learning paradigm still has a gap of about 19% with the state-of-the-art ODNNs, the number of parameters of the guided student networks is smaller than the state-of-the-art networks by several or even dozens of times. These results establish a paradigm for overcoming fundamental limitations in ONN optimization, paving the way for a new era in which ONNs can solve large-scale practical problems through photonic–electronic and photonic–photonic co-design.

Integrating interlayer ventilation into phase change walls: Comprehensive performance and optimization in summer

Solar thermoelectric generators (STEGs) integrating phase change materials (PCMs) have attracted increasing attention in the past few years. However, the study of synergistic effects of heat transfer of PCM and cooling condition of heat sink on thermoelectric generator (TEG) performance is not sufficiently in-depth. This study improves the fins arrangement in phase change energy storage devices through simulation and experiments to enhance heat transfer, while also considering the influence of a PCM container material. Additionally, we compared the effects of TEG numbers and cooling conditions on system efficiency. The results indicate that the parallel arrangement of heat-conducting fins in the phase change heat storage container can completely melt PCM within half an hour, while the S-shape arrangement still remains 20% and has not melted under the same conditions. On this basis, when the solar radiation intensity is 1.6 W/cm2, PCM-STEG with six TEGs can obtain 1895 J of electrical energy under water-cooling conditions at a temperature of 30 °C and a flowrate of 25 mL/min.