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检索条件"任意字段=Conference on Photonic Devices and Algorithms for Computing VIII"

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All-Optical computing for Super-Resolution Direction of Arrival Estimation

All-Optical Computing for Super-Resolution Direction of Arri...

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Asia Communications and photonics conference and Exhibition (ACP)

作者： Sheng Gao Hang Chen Haiou Zhang Zhi Sun Yuan Shen Xing Lin Department of Electronic Engineering Tsinghua University Beijing China

ISBN: (数字)9798350379266

ISBN: (纸本)9798350379273

Direction of arrival estimation technology is the foundation for communications, radar, navigation, etc. However, conventional electronic devices for DOA estimation require expensive RF circuits, high-precision analogue-to-digital converters and complex digital signal processing algorithms. Here, we propose a super-resolution diffractive neural network (S-DNN) to directly process electromagnetic (EM) waves for super-resolution DOA estimation over a broadband frequency range. The multilayer meta-structures of the S-DNN produces super-oscillatory angular responses in local angular range, which can perform all-optical DOA estimation with angular resolution beyond the diffraction limit. Space-time multiplexing of passive and reconfigurable S-DNNs is used to achieve high-resolution DOA estimation over a wide field of view. Experiments show that the angular resolution of the S-DNN is four times higher than the diffraction limited resolution. The fabricated S-DNN can be used for superresolution DOA estimation of multiple coherent sources over a 5 GHz frequency bandwidth, with an estimation delay that is in principle two to four orders of magnitude lower than commercial devices. We also use the all-optical DOA estimation capability of the S-DNN to provide angular direction to reconfigurable intelligent surface, to achieve low-latency and low-power integrated sensing and communication. Our work is an important step towards enabling various wireless sensing and communication tasks using photonic computing processors that outperform electronic computing in both computational paradigm and performance.

关键词： Wireless communication Radio frequency Wireless sensor networks Direction-of-arrival estimation Program processors Diffraction Superresolution Neural networks Estimation photonics

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photonic-aware Neural Networks for Packet Classification in beyond 5G Networks 13

Photonic-aware Neural Networks for Packet Classification in ...

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13th International conference on the Network of the Future, NoF 2022

作者： Paolini, Emilio Civerchia, Federico De Marinis, Lorenzo Valcarenghi, Luca Maggiani, Luca Andriolli, Nicola Scuola Superiore sant'Anna Pisa56124 Italy TeCIP Institute Departments of Excellence Robotics & AI Italy National Research Council of Italy CNR-IEIIT Pisa56122 Italy Sma-RTy Italiat SRL Carugate20061 Italy

ISBN: (数字)9781665472548

ISBN: (纸本)9781665472548

The benefits introduced by novel network technologies such as 5G and beyond, including low latency and support for billions of devices, have the potential to transform the lives of people. However, the features promised by these new technologies have also attracted malicious actors, with various motivations for attacking the network infrastructure, from cybercrime-based frauds to political goals. Thus, to enable the full potential of the emerging network technologies, it is necessary to take into accounts these attacks and develop tailored countermeasures. One future direction in mitigating the risks of potential attacks is the automatic classification of malicious packets, with the possibility to drop them if classified in the attack category. Hence, in this context, we propose a solution based on Neural Networks (NNs) to automatically classify packets into two classes, i.e., benign and attack, directly in the Radio Access Network (RAN), specifically inspecting packets when they are relayed at the next generation eNB (gNB)-Central Unit (CU) level. Since NNs can be computationally intensive algorithms, potentially increasing the latency of the network, we decide to leverage photonic-Aware Neural Network (PANN), photonic accelerators able to perform NN computations in the analog optical domain and with time-of-flight latency. We devised two different PANN architectures, considering different photonic constraints. The classification performance of the two architectures has been assessed on the CICIDS-2017 dataset and compared with electronic counterparts. Results proved that the F1-score loss due to underlying hardware constraints is negligible, paving the way for PANN applications in next generation networks. © 2022 IEEE.

关键词： 5G mobile communication systems

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Reservoir Based Edge Training on RF Data To Deliver Intelligent and Efficient IoT Spectrum Sensors 7

Reservoir Based Edge Training on RF Data To Deliver Intellig...

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7th IEEE International conference on Smart computing (SMARTCOMP)

作者： Kokalj-Filipovic, Silvija Toliver, Paul Johnson, William Miller, Rob Peraton Labs Inc Basking Ridge NJ 07920 USA

ISBN: (纸本)9781665412520

Current radio frequency (RF) sensors at the Edge lack the computational resources to support practical, in-situ training for intelligent spectrum monitoring. This is true for sensor data classification in general. We propose a solution via Deep Delay Loop Reservoir computing (DLR), a processing architecture that supports general machine learning algorithms on compact mobile devices by leveraging delay-loop reservoir computing in combination with innovative electro-optical hardware. With both digital and photonic realizations of our design of the loops, DLR delivers reductions in form factor, hardware complexity and latency, compared to the State-of-the-Art (SoA). The main impact of the reservoir is to project the input data into a higher dimensional space of reservoir state vectors in order to linearly separate the input classes. Once the classes are well separated, traditionally complex, power-hungry classification models are no longer needed for the learning process. Yet, even with simple classifiers based on Ridge regression (RR), the complexity grows at least quadratically with the input size. Hence, the hardware reduction required for training on compact devices is in contradiction with the large dimension of state vectors. DLR employs a RR-based classifier to exceed the SoA accuracy, while further reducing power consumption by leveraging the architecture of parallel (split) loops. We present DLR architectures composed of multiple smaller loops whose state vectors are linearly combined to create a lower dimensional input into Ridge regression. We demonstrate the advantages of using DLR for two distinct applications: RF Specific Emitter Identification (SEI) for IoT authentication, and wireless protocol recognition for IoT situational awareness.

关键词： IoT reservoir computing edge computing in-situ training delay loops wireless security specific emitter identification RF fingerprinting RF protocol recognition

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Optimal Mapping for Near-Term Quantum Architectures based on Rydberg Atoms 40

Optimal Mapping for Near-Term Quantum Architectures based on...

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40th IEEE/ACM International conference on Computer Aided Design (ICCAD)

作者： Brandhofer, Sebastian Buechler, Hans Peter Polian, Ilia Univ Stuttgart Ctr Integrated Quantum Sci & Technol Inst Comp Architecture & Comp Engn Stuttgart Germany Univ Stuttgart Ctr Integrated Quantum Sci & Technol Inst Theoret Phys 3 Stuttgart Germany

ISBN: (纸本)9781665445078

Quantum algorithms promise quadratic or exponential speedups for applications in cryptography, chemistry and material sciences. The topologies of today's quantum computers offer limited connectivity, leading to significant overheads for implementing such quantum algorithms. One-dimensional topology displacements that remedy these limits have been recently demonstrated for architectures based on Rydberg atoms, and they are possible in principle in photonic and ion trap architectures. We present the first optimal quantum circuit-to-architecture mapping algorithm that exploits such one-dimensional topology displacements. We benchmark our method on quantum circuits with up to 15 qubits and investigate the improvements compared with conventional mapping based on inserting swap gates into the quantum circuits. Depending on underlying technology parameters, our approach can decrease the quantum circuit depth by up to 58% and increase the fidelity by up to 29%. We also study runtime and fidelity requirements on one-dimensional displacements and swap gates to derive conditions under which one-dimensional topology displacements provide benefits.

关键词： Quantum computing Quantum Circuit Mapping Topology Displacements Rydberg Atoms CAD

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Design Technology for Scalable and Robust photonic Integrated Circuits 38

Design Technology for Scalable and Robust Photonic Integrate...

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38th IEEE/ACM International conference on Computer-Aided Design (ICCAD)

作者： Zhao, Zheng Gu, Jiaqi Ying, Zhoufeng Feng, Chenghao Chen, Ray T. Pan, David Z. Univ Texas Austin Dept Elect & Comp Engn Austin TX 78712 USA Univ Texas Austin Dept Mat Sci & Engn Austin TX 78712 USA

ISBN: (纸本)9781728123509

photonic integrated circuit (PIC), as a promising alternative to traditional CMOS circuit, has demonstrated the potential to accomplish on-chip optical signal transmission and computations in ultra-high speed and/or low power consumption. One of the critical challenges of PIC, however, is that its scalability and robustness are limited by cascaded optical power loss and noise error. In this paper, we analyze the scalability and noise robustness challenges facing photonic integrated circuits, for two representative PIC applications: logic computing and neural networks. Automated design algorithms and learning methodologies are proposed to resolve these issues.

关键词： Computation theory

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Efficient auction mechanism for edge computing resource allocation in mobile blockchain 21

Efficient auction mechanism for edge computing resource allo...

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21st IEEE International conference on High Performance computing and Communications, 17th IEEE International conference on Smart City and 5th IEEE International conference on Data Science and Systems, HPCC/SmartCity/DSS 2019

作者： Liu, Xuelian Wu, Jigang Chen, Long Xia, Chengpeng School of Computer Science and Technology Guangdong University of Technology Guangzhou China

ISBN: (纸本)9781728120584

Blockchain technique has been widely applied in various fields, such as finance, Internet of Things, law, etc. However, it is a challenge to apply blockchain technique for mobile applications, as the mobile devices cannot afford computing resources required by the mining processes. This paper proposes a mechanism based on a combinatorial double auction to offload the mining process of miners to the edge servers. The mechanism is formulated as a resource allocation problem. The corresponding allocation algorithms and payment scheme are proposed to allocate resources and calculate trade prices, respectively. Moreover, this paper proves that the proposed mechanism is efficient in terms of computation, and it satisfies three properties of economic auction which are budget balance, individual rationality and truthfulness. Experimental results show that the proposed mechanism is able to yield higher total utility, together with good scalability. For the case of 900 miners in the auction, the total utility of the proposed mechanism is higher than two existing works by 39.2% and 569.9% on average, respectively. © 2019 IEEE.

关键词： Blockchain

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Environment-mediated coupling of autonomous sound-generating systems in live performance: An overview of the machine milieu project 14

Environment-mediated coupling of autonomous sound-generating...

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14th Sound and Music computing conference, SMC 2017

作者： Sanfilippo, Dario Di Scipio, Agostino Reid School of Music University of Edinburgh United Kingdom Conservatorio di L'Aquila Université Paris-VIII France

ISBN: (纸本)9789526037295

Today, a large variety of technical configurations are used in live performance contexts. In most of them, computers and other devices act usually as powerful yet subordinated agencies, typically piloted by performers: with few notable exceptions, large-scale gestures and structural developments are left either to the performer's actions or to well-planned automations and/or composing algorithms. At the same time, the performance environment is either ignored or'tuned out', ideally kept neutral with regard to the actual sound events and the overall performance process. This paper describes a different approach. The authors investigate the complex dynamics arising in live performance when multiple autonomous sound systems are coupled through the acoustic environment. In order to allow for more autonomous and adaptive - or better: ecosystemic - behaviour on the part of the machines, the authors suggest that the notion of interaction should be replaced with that of a permanent and continuing structural coupling between machine(s), performer(s) and environment(s). More particularly, the paper deals with a specific configuration of two (or more) separate computer-based audio systems co-evolving in their autonomic processes based on permanent mutual exchanges through and with the local environment, i.e., in the medium of sound only. An attempt is made at defining a self-regulating, situated, and hybrid dynamical system having its own agency and manifesting its potential behaviour in the performance process. Human agents (performers) can eventually intrude and explore affordances and constraints specific to the performance ecosystem, possibly biasing or altering its emergent behaviours. In so doing, what human agents actually achieve is to specify their role and function in the context of a larger, distributed kind of agency created by the whole set of highly interdependent components active in sound. That may suggest new solutions in the area of improvised or structured p

关键词： Audio systems

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photonic Networks and devices, Networks 2018

Photonic Networks and Devices, Networks 2018

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photonic Networks and devices, Networks 2018

ISBN: (纸本)9781943580439

This proceedings contains 43 papers. AO investigates the commonality and possible synergies between the adaptive optics methods developed and used by various communities pursuing different applications. Topics include AO systems/component technologies, wavefront correction optics, control algorithms, and signal processing electronics used in adaptive optics implementations as well as limitations and novel applications. Also known as: Adaptive Optics: Analysis, Methods and Systems;Adaptive Optics: Methods, Analysis and Applications. The conference topics include: Design problems towards reliable SDN Networks;Multi-failure Resiliency and Cost-effectiveness in Transport Networks: A Contradiction?;Dynamic (re)configuration of optical networks based on monitoring information: field trial;Robust Optical Networks with Emerging Coherent Technologies and Traffic Trends;Distributed Machine Learning Location Algorithm for Reliable C-RAN;WDM routing for edge data centers and disaggregated computing;Disaggregation at the Optical Layer: toward an Optical White Boxes Ecosystem?;Silicon photonics Enabling the Disaggregated Data Center;Surface Micromachined Silicon photonic MEMS: A Scalable Technology Platform for photonic Network Components;Rethinking data storage for the zettabyte cloud era: The journey from metal to glass;Advanced Photoreceiver Technology for Space Division Multiplexing Communication;Wavelength-Selective Switching for Mode-Division Multiplexing;An Inter-Modal-Coupling-Aware Heuristic Algorithm for Routing, Spectrum and Mode Assignment in Few-Mode Optical Networks;First WDM-SDM Optical Network with Spatial Sub-Group Routing ROADM Nodes Supporting Spatial Lane Changes;1,024×1,024 Optical Circuit Switch Using Wavelength-Tunable and Bandwidth-Variable Silicon photonic Filter;Impact of Contention/-less ROADM Nodes on Total Network Node Switching Capacity;Technologies for the Implementation of Spectrally-Spatially Flexible Optical Networks (SS-FONs);Towards Entangled P

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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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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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