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

共 215 条记录，以下是1-10 订阅

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Monolithically integrated optical computing component for photonic matrix multiplication based on electro-optically modulated vertical-cavity surface-emitting lasers 11

Monolithically integrated optical computing component for ph...

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conference on Semiconductor Lasers and Laser Dynamics XI

作者： Ledentsov, N., Jr. Dijkstra, J. Makarov, Yu Shchukin, V. A. Chorchos, L. Kropp, J-R Pernice, W. H. P. Ledentsov, N. N. VI Syst GmbH Hardenbergstr 7 D-10623 Berlin Germany Heidelberg Univ Kirchhoff Inst Phys Grabengasse 1 D-69117 Heidelberg Germany

ISBN: (纸本)9781510673236;9781510673229

Leading-edge machine learning algorithms require large amounts of matrix multiplications, which absorb significant computational resources in modern digital electronic systems. Analogue optical computing systems consisting of light sources, modulators and receivers may perform these operations with much higher efficiency. We introduce an integrated device based on electro-optically modulated (EOM) vertical-cavity surface-emitting lasers (VCSELs) that can perform analog multiplication at >28 GHz and at <20 mW power consumption. Due to its monolithic integration, the EOM VCSEL may be used as a building block for integrated optical computing devices. Development of such devices can help create 3-dimensionally integrated computing and communication systems.

关键词： vertical cavity surface-emitting laser optical computing analogue computing photonic integrated circuits

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Diamond integrated photonics for quantum computing applications 3

Diamond integrated photonics for quantum computing applicati...

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conference on Integrated photonics Platforms iii

作者： Ulanov, Mark Jin, Lin Pernice, Wolfram H. P. Heidelberg Univ Kirchhoff Inst Phys Neuenheimer Feld 227 D-69120 Heidelberg Germany Univ Munster Inst Phys Wilhelm Klemm Str 10 D-48149 Munster Germany

ISBN: (纸本)9781510673434;9781510673427

Integrated diamond photonics presents one of the most promising platforms for solid-state quantum computing due to diamond's high refractive index, large electronic bandgap and its large number of defects with highly stable fluorescence of single photons and coherent electron spin control like the silicon vacancy. This work presents the design, simulation, fabrication and measurement of free-standing diamond photonic crystal cavities from bulk single-crystal diamond substrate for emission enhancement of the SiV-. Simulation yields Q-factors of up to 2.4 million and normalised mode volumes down to 0.52. Confocal measurements of the fabricated devices yield Q-factors of up to 1800 near the SiV- zero phonon line. These diamond resonators offer a promising approach to realizing large-scale diamond quantum registers.

关键词： diamond photonics Purcell enhancement colour centres in diamond nanobeam resonators photonic crystal cavity

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CMOS-compatible electro-optical SRAM cavity device based on negative differential resistance 3

CMOS-compatible electro-optical SRAM cavity device based on ...

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conference on Integrated photonics Platforms iii

作者： Gherabli, Rivka Zektzer, Roy Grajower, Meir Shappir, Joseph Frydendahl, Christian Levy, Uriel Hebrew Univ Jerusalem Fac Sci Ctr Nanosci & Nanotechnol Dept Appl Phys IL-91904 Jerusalem Israel

ISBN: (纸本)9781510673434;9781510673427

We experimentally demonstrate a new electro-optic SRAM element fully CMOS compatible. Inspired by the Esaki diode, presenting negative differential resistance (NDR), we designed a new type of NDR diode based on a horizontal PN junction and a region with higher acceptor concentration, P+, in silicon. We embedded the new NDR into a photonic micro-ring resonator to enable a bistable device with electrical and optical readout capabilities. Our device is remarkable for its simplicity, CMOS compatibility and its low power consumption around the nanowatt, but it's also an important steppingstone on the way to new nonlinear electro-optic and neuromorphic computing structures.

关键词： integrated photonic devices CMOS electro-optic SRAM micro-ring resonator

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Post-fabrication tuning of photonic integrated circuits via silicon ion implantation 3

Post-fabrication tuning of photonic integrated circuits via ...

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conference on Integrated photonics Platforms iii

作者： Varria, Akhil Taheriniya, Shabnam Bruckerhoff-Plueckelmanna, Frank Pernice, Wolfram H. P. Univ Munster Inst Phys Heisenbergstr 11 D-48149 Munster Germany Heidelberg Univ Kirchhoff Inst Phys Neuenheimer Feld 227 D-69120 Heidelberg Germany

ISBN: (纸本)9781510673434;9781510673427

Integrated photonic circuits represent a significant advancement in optical technology, providing notable benefits in speed, efficiency, and miniaturization. By consolidating various optical components onto a single chip, these circuits enable precise manipulation and control of light. As a result, they are playing a pivotal role in technologies such as high-speed data interconnects, sensors, artificial intelligence accelerators, and quantum computing. However, one of the key challenges that hinders the widespread adoption of integrated optics is fabrication imperfections. This work(1) highlights a scalable and non-volatile technique for post-fabrication tuning of photonic computational memories by automated silicon ion implantation to precisely align high-quality resonant devices to targeted wavelengths. Spectral shifts ranging from less than 10 pm to several nanometers are obtained showing long-term stability while adding negligible loss.

关键词： integrated photonics wavelength division multiplexing ring resonators post-fabrication tuning

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Experimental Simulation of Loop Quantum Gravity on a photonic Chip

Experimental Simulation of Loop Quantum Gravity on a Photoni...

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Quantum 2.0, QUANTUM 2024 in Quantum 2.0 conference and Exhibition

作者： Anguita, Malaquias Correa van der Meer, Reinier Huang, Zichang Qu, Dongxue Hooijschuur, Peter Liu, Hongguang Han, Muxin Renema, Jelmer J. Cohen, Lior MESA+ Institute for Nanotechnology University of Twente P.O.box 217 Enschede7500 AE Netherlands State Key Laboratory of Surface Physics Department of Physics Center for Field Theory and Particle Physics Institute for Nanoelectronic devices and Quantum computing Fudan University Shanghai200433 China Department of Physics Florida Atlantic University 777 Glades Road Boca RatonFL33431 United States Department Physik Institut für Quantengravitation Theoretische Physik III Friedrich-Alexander Universität Erlangen-Nürnberg Staudtstr. 7/B2 Erlangen91058 Germany Department of Electrical Computer and Energy Engineering University of Colorado Boulder BoulderCO80309 United States

We experimentally simulate a basic transition amplitude of Loop Quantum Gravity on an integrated photonic chip, and show that this amplitude falls within 4% error from the theoretical prediction. © 2024 The Autho... 详细信息

ISBN: (纸本)9781557525185

关键词： photonic integrated circuits

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Physical Aware Clustering Training Method for Integrated photonic Convolution Neural Network with Nonlinear Distributed Weights

Physical Aware Clustering Training Method for Integrated Pho...

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Asia Communications and photonics conference (ACP) / International conference on Information photonics and Optical Communications (IPOC)

作者： Jiang, Yue Zhang, Wenjia Liu, Xuying Zhu, Wenyu He, Zuyuan Shanghai Jiao Tong Univ State Key Lab Adv Opt Commun Syst & Networks Shanghai 200240 Peoples R China

ISBN: (纸本)9781665481557

The computing power of the photonic Convolution Neural Network (CNN) have achieved Tera-level operations per second (OPS) for supporting machine vision algorithms. However, restrained by the nonlinear characteristics of weighting devices, it is hard to realize convergent training of photonic CNN under poor controlling precision. In this paper we proposed a Physical Aware Clustering training method where the Physical Aware Cluster Quantizer is embed with the straight through estimator (STE) algorithm for integrated photonic CNN with quantized and nonlinear distributed weights. Experiment shows that, by employing the upgraded STE methods (namely, STE plus), the photonic CNN using micro ring weighting bank and PAM4 controlling modules achieves the nearly 99.3% accuracy ratio for Fashion MNIST recognition task, whereas only 41.5% of that by using baseline STE training algorithms with the same physical devices.

关键词： component photonic integrated devices Optical computing photonic convolution neural network

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Non-Volatile Memristive iii-V/Si photonics

Non-Volatile Memristive III-V/Si Photonics

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IEEE Silicon photonics conference (Siphotonics)

作者： Cheung, Stanley Tossoun, Bassem Yuan, Yuan Hu, Yingtao Kurczveil, Geza Peng, Yiwei Liang, Di Beausoleil, Raymond G. Hewlett Packard Enterprise Large Scale Integrated Photon Lab Milpitas CA 95035 USA

ISBN: (纸本)9781665486552

Over the past few decades, processor performance has scaled accordingly to Moore's Law, but there remains an architectural limit known as the von-Neumann bottleneck. The need to transfer massive amounts of data between processor and memory has resulted in an intrinsic limit of bandwidth and reach, thus resulting in increased interconnect power consumption. As a major step towards breaking this bottleneck, especially for photonic neuromorphic computing, we have leveraged our heterogeneous iii-V/Si optical interconnect platform to demonstrate non-volatile iii-V/Si photonics based on resistive random access memory (ReRAM) or known as memristors [1], [2]. We review our work on memristive ring resonators and Mach-Zehnder Interferometer (MZI) devices

关键词： non-volatile memristors heterogeneous integration

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Silicon photonics for energy-efficient neuromorphic computing 3

Silicon photonics for energy-efficient neuromorphic computin...

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conference on Emerging Applications in Silicon photonics iii

作者： Tossoun, Bassem Hewlett Packard Enterprise Hewlett Packard Labs 820 N McCarthy Blvd Milpitas CA 94035 USA

ISBN: (纸本)9781510657403;9781510657410

New machine learning algorithms such as deep neural networks and the availability of large datasets have created a large drive towards new types of hardware capable of executing these algorithms with higher energy-efficiency. Recently, silicon photonics has emerged as a promising hardware platform for neuromorphic computing due to its inherent capability to process linear and non-linear operations and transmit a high bandwidth of data in parallel. At Hewlett Packard Labs, an energy-efficient dense-wavelength division multiplexing (DWDM) silicon photonics platform has been developed as the underlying foundation for innovative neuromorphic computing architectures. The latest research on our silicon photonic neuromorphic platform will be presented and discussed.

关键词： silicon photonics neuromorphic computing optical computing

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Entanglement-enhanced Quantum Reinforcement Learning: an Application using Single-Photons 5

Entanglement-enhanced Quantum Reinforcement Learning: an App...

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2024 International conference on Quantum computing and Engineering

作者： Gaspar, Joaquim M. Bergerault, Alexandre Apostolou, Vassilis Ricou, Arno Quandela F-91300 Massy France Inst Polytech Paris ENSTA Paris F-91120 Palaiseau France

ISBN: (纸本)9798331541378

Quantum Reinforcement Learning (QRL) is an emerging field with many novel approaches suitable for noisy intermediate-scale quantum (NISQ) devices being proposed recently. Projective Simulation (PS) is a classical machine learning method based on random walks in a Directed Acyclic Graph (DAG), and is mainly used to train agents in a Reinforcement Learning setup. This graph corresponds to the Episodic Compositional Memory (ECM) of the agent. For the quantum version of PS we use quantum walks of single photons in quantum optical circuits, which go through tunable components (beamsplitters and phase shifters). We call this specific model Quantum Optical Projective Simulation (QOPS). In this work, we propose a general framework to translate any 2-layer ECM to a quantum optical circuit and an algorithm for solving quantum strategic games using QOPS. Moreover, we introduce QOPS with an entangled state as input. The application that we are using to evaluate the performance of the framework is the Prisoner's Dilemma. We executed the implementation using Altair's noisy simulator, designed to emulate a single photon-based quantum processor by Quandela. The algorithm provided promising results even in the presence of noise, which makes it a strong candidate for runs on single-photon -based quantum processors.

关键词： quantum reinforcement learning quantum machine learning variational quantum algorithms photonic quantum computing projective simulation quantum games

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Condar: Context-Aware Distributed Dynamic Object Detection on Radar Data

Condar: Context-Aware Distributed Dynamic Object Detection o...

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2024 Military Communications conference

作者： Harshbarger, Ian Zejda, Colin Levorato, Marco Univ Calif Irvine Irvine CA 92697 USA

ISBN: (纸本)9798350374247;9798350374230

Deep neural networks are an established class of algorithms widely used for real-time data analysis in computer vision-based mobile applications. However, on one hand the constrained resources - computing power, energy reservoir and memory - available to mobile devices clash with the complexity of high-performance neural models. On the other hand, offloading the execution of the computing task to a wirelessly connected edge/cloud server requires the transfer of information rich signals over volatile and capacity constrained channels. To address this issue, the Split computing (SC) paradigm has been proposed. In SC frameworks, a baseline DNN model is used to create two models, namely head and tail models, that are executed at a mobile device and edge/cloud server, respectively. In this work, a SC paradigm - Condar - is presented that introduces several core design and conceptual innovations: (i) we target object detection on radar data rather than 2D RGB images;(ii) we design encoders/decoder structures that are specialized to the specific operating environment;(iii) we develop a multi-branched model that can dynamically select the best performing encoder while executing the main model. Our design is based on the CenterNet model with a split ResNet50 backbone trained on the RADIATE dataset's radar images of resolution 1152x1152. Our results show that Condar has performance analogous to that of a Generalized CenterNet Model with a difference of 4.41%, 1.72%, and 2.18% on mAP@50, mAP@75, and mAP@COCO respectively, while simultaneously performing compression on the features of the data to 15.5KB. This datasize is smaller than lowest possible compression data size of JPEG compression at 21KB, which results in a considerable performance degradation.

关键词： Dynamic Deep Neural Networks Edge computing Split computing Radar Object Detection

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