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Automated design of multi junction solar cells by genetic approach: Reaching the >50% efficiency target

SOLAR ENERGY MATERIALS AND SOLAR CELLS 2018年 181卷 30-37页

作者： Cicic, Slobodan Tomic, Stanko Univ Salford Sch Comp Sci & Engn Joule Phys Lab Manchester M5 4WT Lancs England

The proper design of the multi junction solar cell (MJSC) requires the optimisation search through the vast parameter space, with parameters for the proper operation quite often being constrained, like the current matching throughout the cell. Due to high complexity number of MJSC device parameters might be huge, which makes it a demanding task for the most of the optimising strategies based on gradient algorithm. One way to overcome those difficulties is to employ the global optimisation algorithms based on the stochastic search. We present the procedure for the design of MJSC based on the heuristic method, the genetic algorithm, taking into account physical parameters of the solar cell as well as various relevant radiative and non-radiative losses. In the presented model, the number of optimising parameters is 5 M + 1 for a series constrained M-jtmctions solar cell. Diffusion dark current, radiative and Auger recombinations are taken into account with actual ASTM G173-03 Global tilted solar spectra, while the absorption properties of individual SCs were calculated using the multi band Icv Hamiltonian. We predicted the efficiencies in case of M = 4 to be 50.8% and 55.2% when all losses are taken into account and with only radiative recombination, respectively.

关键词： Multi junction solar cells Current matching iii-V semiconductors Auger effect Genetic algorithm

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Evolution of photonic Integrated Circuits 75

Evolution of Photonic Integrated Circuits

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75th Annual Device Research conference (DRC)

作者： Bowers, John E. Univ Calif Santa Barbara Dept Elect & Comp Engn Santa Barbara CA 93106 USA

ISBN: (纸本)9781509063284

photonic Integrated Circuits (PICs) are evolving rapidly for applications from interconnects to sensors. We present the evolution of four different platfolins for photonic integration: pure iii-V/InP, pure SOI (monolithic silicon photonics), iii-V heterogeneously integrated on SOI (heterogeneous integration), and iii-V epitaxially grown on silicon. Presently, thousands of optical devices are integrated into PICs to achieve multiterabit per second transceivers, LIDARs, and chemical sensors. photonic integrated circuits (PICs) are currently fueling high bandwidth density interconnection links, and are primed to serve other growth markets such as LIDAR and chip-scale wearable sensors. Various material platfolins for photonic integration exist, each with their own unique advantages and shortcomings. Four different approaches are differentiated among these based on potential to deliver high perfounance with a monolithically integrated solution combining both active and passive functionalities, and the ability to scale at low cost with the expected growth of the overall photonic integrated circuit market: 1) pure iii-V (InP), 2) silicon on insulator (SOI), 3) heterogeneous integration of iii-V on SOI and 4) epitaxial growth of iii-V on silicon. The level of integration (e.g. number of devices integrated onto one waveguide) for the first three approaches that has been demonstrated to date is summarized in Fig. 1. In the sections below, a brief description of each of the four approaches is given, along with some prominent commercial examples where applicable.

关键词： Estimation Field programmable gate arrays OFDM Modulation Filtration Clocks Signal processing algorithms

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ConvLight: A Convolutional Accelerator with Memristor integrated photonic computing 24

ConvLight: A Convolutional Accelerator with Memristor integr...

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IEEE 24th International conference on High Performance computing Workshops (HiPCW)

作者： Dang, Dharanidhar Dass, Jyotikrishna Mahapatra, Rabi Texas A&M Univ Dept Comp Sci & Engn College Stn TX 77843 USA

ISBN: (纸本)9781538622933

Neuromorphic computing is a promising candidate to accelerate big data processing. Recently, several attempts have been made to design neuromorphic accelerators for popular machine learning algorithms, such as reservoir computing, deep learning, spiking neurons etc. Deep learning accelerator which involves convolutional neural networks (CNNs) have received widespread attention for their accuracy and efficiency. This paper proposes ConvLight, a novel deep learning accelerator based on memristor integrated photonic computing framework. While the use of on-chip photonic circuits for analog computing is well known, no prior work has demonstrated a full-fledged accelerator based on photonic components. In particular, this paper makes the following novel contributions: (i) A multilayer deep learning architecture design is proposed using compute efficient memristors and photonic components for the first time. (ii) A pipelined design for each CNN layer is presented for maximizing throughput and enabling parallelism across the layers. (iii) Simulation of ConvLight architecture with standard photonic tools for demonstrating the execution of DNN and CNN workloads yielding 25X, 60X, and 40X improvements in computational efficiency, throughput, and energy efficiency (respectively) compared to state-of-the-art design.

关键词： Neuromorphic computing big data machine learning photonic accelerator DNN CNN

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From Cloud computing to Fog computing: Unleash the Power of Edge and End devices 9

From Cloud Computing to Fog Computing: Unleash the Power of ...

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9th IEEE International conference on Cloud computing Technology and Science (CloudCom)

作者： Hong, Hua-Jun Natl Tsing Hua Univ Dept Comp Sci Hsinchu Taiwan

ISBN: (纸本)9781538606926

Fog computing extends cloud computing to end devices, in order to better support time dependent, location dependent, massive scale, and latency sensitive applications. In this paper, we propose a fog computing ecosystem, implement a real testbed, and evaluate it with diverse usage scenarios. More specifically, we study three usage scenarios and optimize our fog computing platform for them. These usage scenarios are: (i) content dissemination in challenged networks, (ii) crowd-sourced fog computing, and (iii) programmable Internet-of-Things analytics. We leverage and enhance open source projects to realize our fog computing platform. We then solve optimization problems in each usage scenario with novel algorithms. Sample results show that our proposed algorithms outperform baseline algorithms by at least 30.3%, 20.0%, and 89.4% in terms of the main performance metrics of the three usage scenarios, respectively. Several ongoing tasks aim to improve our fog computing platform for: (i) network resource provision, (ii) system dynamics adaption, and (iii) device availability prediction.

关键词： Edge computing Prediction algorithms Cloud computing Heuristic algorithms Animation Rendering (computer graphics) Optimization

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photonic Reservoir Computer With Output Feedback for Chaotic Time Series Prediction

Photonic Reservoir Computer With Output Feedback for Chaotic...

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International Joint conference on Neural Networks (IJCNN)

作者： Antonik, Piotr Hermans, Michiel Haelterman, Marc Massar, Serge Univ Libre Bruxelles Lab Informat Quant Ave FD Roosevelt 50CP 224 Brussels Belgium Univ Libre Bruxelles Serv OPERA Photon Ave FD Roosevelt 50CP 194-5 Brussels Belgium

ISBN: (纸本)9781509061822

Reservoir computing is a bio-inspired computing paradigm for processing time dependent signals. It can be easily implemented in hardware. The performance of these analogue devices matches digital algorithms on a series of benchmark tasks. Their capacities could be extended by feeding the output signal back into the reservoir, which would allow them to be applied to various signal generation tasks. In practice, this requires a high-speed readout layer for real-time output computation. Here we achieve this by means of an FPGA chip, and demonstrate the first photonic reservoir computer with output feedback. We test our setup on the Mackey-Glass chaotic time series generation task and obtain interesting prediction horizons, comparable to numerical simulations, with ample room for further improvement. Our work thus demonstrates the potential offered by the output feedback and opens a new area of novel applications for photonic reservoir computing.

关键词： Benchmarking

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Silicon photonics for Optical computing, Interconnects and Sensing 22

Silicon Photonics for Optical Computing, Interconnects and S...

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22nd Microoptics conference (MOC)

作者： Chen, Ray T. Univ Texas Austin Microelect Res Ctr Austin TX 78758 USA Omega Opt 8500 Shoel Creek Blvd Austin TX 78757 USA

ISBN: (纸本)9784863486096

Silicon photonics has been an active research topic for the past twenty years. The maturity of silicon nanofabrication and the transparency of silicon material at C-and L-band are a critical factor for the realization of silicon photonics for real applications. However, due to the intrinsic nature of indirect bandgap, hybrid integration with external iii-V based lasers is the trend for integration. In this talk, we will focus on several key application areas. Figure 1 shows the system architecture to be demonstrated using iii-V and silicon integrated photonic devices where energy consumption and bandwidth can be significantly improved.

关键词： Silicon Biosensors photonics Optical ring resonators photonic crystals Optical waveguides

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Distributed algorithms on IoT devices: Bully Leader Election

Distributed Algorithms on IoT Devices: Bully Leader Election

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International conference on Computational Science and Computational Intelligence (CSCI)

作者： Mendez, Mariano Tinetti, Fernando G. Duran, Adrian M. Obon, Daniel A. Bartolome, Natalia G. Univ Buenos Aires Fac Ingn Dept Comp Sci Caba Argentina UNLP Fac Informat III LIDI Comis Inv Cient Prov Bs As La Plata Buenos Aires Argentina

ISBN: (纸本)9781538626528

We present in this paper the implementation of a well-known coordination algorithm on specific and low-performance IoT (Internet of Thing) devices. The implementation of the bully algorithm for leader election is achieved in a two-stage process: a) an IoT independent implementation, made in a high level progamming language (Java, in particular), and b) the implementation on the IoT devices, where several limitations and characteristics have to be taken into account. We have used this algorithm for coordination of a set of small cars. Beyond the specific implementation, this work has two main underlying contributions: a) show that it is possible to take advantage of many algorithms and results already proven to be useful in the area of distributed computing, and b) show that IoT devices limitations (e.g. in computing power and storage) do not necessarily imply that useful algorithms cannot be used.

关键词： Distributed System Leader Election Internet of Things

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Chaotic Time Series Prediction Using a photonic Reservoir Computer with Output Feedback 31

Chaotic Time Series Prediction Using a Photonic Reservoir Co...

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31st AAAI conference on Artificial Intelligence

Reservoir computing is a bio-inspired computing paradigm for processing time dependent signals (Jaeger and Haas 2004;Maass, Natschlager, and Markram 2002). It can be easily implemented in hardware. The performance of these analogue devices matches digital algorithms on a series of benchmark tasks (see e.g. (Soriano et al. 2015) for a review). Their capacities could be extended by feeding the output signal back into the reservoir, which would allow them to be applied to various signal generation tasks (Antonik et al. 2016b). In practice, this requires a highspeed readout layer for real-time output computation. Here we achieve this by means of a field-programmable gate array (FPGA), and demonstrate the first photonic reservoir computer with output feedback. We test our setup on the Mackey-Glass chaotic time series generation task and obtain interesting prediction horizons, comparable to numerical simulations, with ample room for further improvement. Our work thus demonstrates the potential offered by the output feedback and opens a new area of novel applications for photonic reservoir computing.

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Cognition-as-a-Service: Arbitrage Opportunity of Cognition algorithms in Cloud computing 1st

Cognition-as-a-Service: Arbitrage Opportunity of Cognition A...

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1st International conference on Computational Intelligence and Informatics (ICCII)

作者： Tripuram, V. S. Reddy Tondamanati, Subash Veeranjaneyulu, N. Vignans Univ Dept Informat Technol Guntur India

ISBN: (纸本)9789811024719;9789811024702

Present era of web services are evolving and extending its services with Cloud computing based remote mobile devices rapidly. The contact of the information world moving from programmable system era to cognitive systems era (Kelly iii John E (2015) IBM Research Whitepaper on Cognitive computing, [ 1]). So the web users have high levels of expectations for the quality of interactions, accuracy of the results and the availability of the services. In the world of cognition and reasoning based web systems to answer the selected problems, to recommend a product information result in right 'context', we need more diverse reasoning strategies and on the fly strategies based intelligent Cognition algorithms to supply Cognition-as-a-service to the cloud based web users. Here we summarize recent works and early findings such as: (1) How natural extension takes place in cognitive knowledge of dynamic web services. (2) How information processing time differs in between HDES and AES. (3) How arbitrage opportunity of cognition algorithm improves the frequency of exact findings in information search.

关键词： AI-artificial intelligence HDES-human decision eco system AIDSS-artificial intelligence decision support system AES-artificial eco system

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Automated tuning, control and stabilization of photonic integrated circuits 3

Automated tuning, control and stabilization of photonic inte...

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conference on Integrated Optics - Physics and Simulations iii

作者： De Aguiar, Douglas O. Annoni, Andrea Peserico, Nicola Guglielmi, Emanuele Carminati, Marco Ferrari, Giorgio Morichetti, Francesco Politecn Milan Dipartimento Elettron Informaz & Bioingn Via Ponzio 35-4 I-20133 Milan Italy

ISBN: (纸本)9781510609853;9781510609860

The complexity scaling of silicon photonics circuits is raising novel needs related to control. Reconfigurable architectures need fast, accurate and robust procedures for the tuning and stabilization of their working point, counteracting temperature drifts originated by environmental fluctuations and mutual thermal crosstalk from surrounding integrated devices. In this contribution, we report on our recent achievements on the automated tuning, control and stabilization of silicon photonics architectures. The proposed control strategy exploits transparent integrated detectors to monitor non-invasively the light propagating in the silicon waveguides in key spots of the circuit. Local monitoring enables the partitioning of complex architectures in small photonic cells that can be easily tuned and controlled, with need for neither preliminary circuit calibration nor global optimization algorithms. The ability to monitor the Quality Of of Transmission (QoT) of the optical paths in photonic Integrated Circuits (PICs) is also demonstrated with the use of channel labelling and non-invasive light monitoring. Several examples of applications are presented that include the automatic reconfiguration and feedback controlled stabilization of an 8x8 switch fabric based on Mach-Zehnder interferometers (MZIs) and the realization of a wavelength locking platform enabling feedback-control of silicon microring resonators (MRRs) for the realization of a 4x10 Gbit/s wavelength-division-multiplexing transmitter. The effectiveness and the robustness of the proposed approach for tuning and stabilization of the presented architectures is demonstrated by showing that no significant performance degradation is observed under uncooled operation for the silicon chip.

关键词： Feedback control microring resonators optical performance monitoring photonic integrated circuits photonic switching systems silicon photonics wavelength locking

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