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检索条件"任意字段=Conference on Neural Network and Distributed Processing"

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MODEL-distributed INFERENCE IN MULTI-SOURCE EDGE networkS

MODEL-DISTRIBUTED INFERENCE IN MULTI-SOURCE EDGE NETWORKS

引用

IEEE International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Li, Pengzhen Seferoglu, Hulya Koyuncu, Erdem Univ Illinois Dept Elect & Comp Engn Chicago IL USA

ISBN: (纸本)9798350302615

distributed inference techniques can be broadly classified into data-distributed and model-distributed schemes. In data-distributed inference (DDI), each worker carries the entire deep neural network (DNN) model, but processes only a subset of the data. However, feeding the data to workers results in high communication costs especially when the data is large. An emerging paradigm is model-distributed inference (MDI), where each worker carries only a subset of DNN layers. In MDI, a source device that has data processes a few layers of DNN and sends the output to a neighboring device. This process ends when all layers are processed in a distributed manner. In this paper, we investigate MDI with multiple sources, i.e., when more than one device has data. We design a multi-source MDI (MS-MDI), which optimizes task scheduling decisions across multiple source devices and workers. Experimental results on a real-life testbed of NVIDIA Jetson TX2 edge devices show that MS-MDI improves the inference time significantly as compared to baselines.

关键词： Model distribution model distributed inference deep neural network (DNN) distributed DNN

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Mapping neural network models onto Transputers

Mapping neural network models onto Transputers

引用

International Joint conference on neural networks - IJCNN-91-Seattle

作者： Barac, Magali E.Azema Comput Sci Dept Univ Coll London UK

ISBN: (纸本)0780301641

Summary form only given, as follows. neural network models are reviewed from the point of view of parallel distributed processing models. More specifically, the interactions between neural models and the computational model implemented by transputer-based machines are identified so as to define a general strategy for mapping neural networks onto transputers, and therefore achieving the design and a prototype of a neural network mapper for transputer-based machines.

关键词： neural networks

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N2SkyC-User Friendly and Efficient neural network Simulation Fostering Cloud Containers 25th

N2SkyC-User Friendly and Efficient Neural Network Simulation...

引用

25th International conference on neural Information processing (ICONIP)

作者： Adamenko, Aliaksandr Fedorenko, Andrii Schikuta, Erich Univ Vienna Fac Comp Sci Wahringerstr 29 A-1090 Vienna Austria

ISBN: (纸本)9783030042240;9783030042233

Y Sky computing is a new computing paradigm leveraging resources of multiple Cloud providers to create a large scale distributed infrastructure. N2Sky is a research initiative promising a framework for the utilization of neural networks as services across many Clouds. This involves a number of challenges ranging from the provision, discovery and utilization of services to the management, monitoring, metering and accounting of the infrastructure. Cloud Container technology offers fast deployment, good portability, and high resource efficiency to run large-scale and distributed systems. In recent years, container-based virtualization for applications has gained immense popularity. This paper presents the new N2SkyC system, a framework for the utilization of neural networks as services, aiming for higher flexibility, portability, dynamic orchestration, and performance by fostering microservices and Cloud container technology.

关键词： Problem solving environment neural networks Cloud computing Containers Microservices

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neural-network PROCESSOR FOR N-MODE FIBER OPTIC SENSORS 4

NEURAL-NETWORK PROCESSOR FOR N-MODE FIBER OPTIC SENSORS

引用

4th International conference on Applications of Artificial neural networks

作者： HOU, H GROSSMAN, B FLORIDA INST TECHNOL DEPT ELECT ENGNMELBOURNEFL 32901

ISBN: (纸本)0819412015

The n-mode fiber optic sensor built has four linearly polarized (LP) modes propagating simultaneously in the fiber, producing a two-dimensional, spatially distributed output intensity pattern. When the fiber is strained, there is a change in fiber parameters. Oscillating and rotating of the pattern caused by coupling between degenerate modes is observed. Thus the processing of this type of output signal becomes one of a two-dimensional image processor. A neural network signal processor employing a back propagation algorithm was used in conjunction with the few mode fiber optic sensor to categorize the spatial output patterns from the sensor, thus converting the optical pattern to its corresponding strain value. The testing results show that the neural network processor is capable of recognizing this kind of image with good accuracy, resulting in strain accuracies within 0.7 percent.

关键词： Fiber optics sensors neural networks Sensors Signal processing Neurons Photodetectors Detection and tracking algorithms Image processing Artificial neural networks Single mode fibers

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distributed LINK SPARSIFICATION FOR SCALABLE SCHEDULING USING GRAPH neural networkS 47

DISTRIBUTED LINK SPARSIFICATION FOR SCALABLE SCHEDULING USIN...

引用

47th IEEE International conference on Acoustics, Speech and Signal processing (ICASSP)

作者： Zhao, Zhongyuan Swami, Ananthram Segarra, Santiago Rice Univ Houston TX 77251 USA US Armys DEVCOM Army Res Lab Adelphi MD USA

ISBN: (纸本)9781665405409

distributed scheduling algorithms for throughput or utility maximization in dense wireless multi-hop networks can have overwhelmingly high overhead, causing increased congestion, energy consumption, radio footprint, and security vulnerability. For wireless networks with dense connectivity, we propose a distributed scheme for link sparsification with graph convolutional networks (GCNs), which can reduce the scheduling overhead while keeping most of the network capacity. In a nutshell, a trainable GCN module generates node embeddings as topology-aware and reusable parameters for a local decision mechanism, based on which a link can withdraw itself from the scheduling contention if it is not likely to win. In mediumsized wireless networks, our proposed sparse scheduler beats classical threshold-based sparsification policies by retaining almost 70% of the total capacity achieved by a distributed greedy max-weight scheduler with 0.4% of the point-to-point message complexity and 2.6% of the average number of interfering neighbors per link.

关键词： Independent set graph neural networks scheduling overhead massive access distributed scheduling

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Response to External Input of Chaotic neural networks Based on Newman-Watts Model

Response to External Input of Chaotic Neural Networks Based ...

引用

International Joint conference on neural networks (IJCNN)

作者： Shibasaki, Manabu Adachi, Masaharu Tokyo Denki Univ Grad Sch Engn Adachi Ku Tokyo 1208551 Japan

ISBN: (纸本)9781467314909

In this paper, the synchronization characteristics in response to external inputs are investigated for chaotic neural networks with coupled lattices based on the Newman-Watts model. The Newman-Watts model was originally proposed with a ring-coupled lattice as its initial structure. However, ring-coupled networks are not suitable for a number of applications, including image processing. Therefore, in this paper, the synchronization characteristics in response to external inputs are investigated in a coupled lattice based on a Newman-Watts network. As a result, we find that synchronized clusters are generated in response to spatially distributed external inputs, and recombination of neurons into clusters occurs in the case that the parameter values of a single neuron correspond to those giving chaotic dynamics. Moreover, we explore the possibility that chaotic dynamics are useful for separating two image segments that have similar grayscale values by using the proposed network with synchronization.

关键词： Synchronization

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WIDE AND DEEP GRAPH neural networkS WITH distributed ONLINE LEARNING

WIDE AND DEEP GRAPH NEURAL NETWORKS WITH DISTRIBUTED ONLINE ...

引用

IEEE International conference on Acoustics, Speech and Signal processing (ICASSP)

作者： Gao, Zhan Ribeiro, Alejandro Gama, Fernando Univ Penn Dept Elect & Syst Engn Philadelphia PA 19104 USA Univ Calif Berkeley Dept Elect Engn & Comp Sci Berkeley CA 94720 USA

ISBN: (纸本)9781728176055

Graph neural networks (GNNs) learn representations from network data with naturally distributed architectures, rendering them well-suited candidates for decentralized learning. Oftentimes, this decentralized graph support changes with time due to link failures or topology variations. These changes create a mismatch between the graphs on which GNNs were trained and the ones on which they are tested. Online learning can be used to retrain GNNs at testing time, overcoming this issue. However, most online algorithms are centralized and work on convex problems (which GNNs rarely lead to). This paper proposes the Wide and Deep GNN (WD-GNN), a novel architecture that can be easily updated with distributed online learning mechanisms. The WD-GNN comprises two components: the wide part is a bank of linear graph filters and the deep part is a GNN. At training time, the joint architecture learns a nonlinear representation from data. At testing time, the deep part (nonlinear) is left unchanged, while the wide part is retrained online, leading to a convex problem. We derive convergence guarantees for this online retraining procedure and further propose a decentralized alternative. Experiments on the robot swarm control for flocking corroborate theory and show potential of the proposed architecture for distributed online learning.

关键词： Graph neural networks graph filters distributed learning online learning convergence analysis

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Fault tolerance in small world cellular neural networks for image processing

Fault tolerance in small world cellular neural networks for ...

引用

21st International conference on Advanced Information networking and ApplicationsWorkshops/Symposia, AINAW'07

作者： Matsumoto, Katsuyoshi Mori, Hideki Uehara, Minoru Department of Open Information Systems Graduate School of Engineering Toyo University Saitama Japan

ISBN: (纸本)0769528473

In this paper, a fault tolerant CNN (Cellular neural network) using a small world network connection for image processing in embedded systems is described. In embedded systems, there are problems such as compactness, power consumption, reliability and speed, so new processor architecture is required. In this paper, we apply a Small World network connection to cellular neural networks. The Small World network features small links to the destination from the place of origin using local connection information of a random nature. The concept is derived from sociology. We then provide neural image processing algorithms suitable for CNN with the Small World network model. Special attention has been given to typical neural algorithms for image processing, such as noise reduction and edge extraction, Further, we are proposing a fault tolerant architecture for the CNN, using majority voting. © 2007 IEEE.

关键词： Cellular neural networks

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A QoS network Architecture to Interconnect Large-Scale VLSI neural networks

A QoS Network Architecture to Interconnect Large-Scale VLSI ...

引用

International Joint conference on neural networks

作者： Philipp, Stefan Schemmel, Johannes Meier, Karlheinz Heidelberg Univ Kirchhoff Inst Phys D-69120 Heidelberg Germany

ISBN: (纸本)9781424435494

This paper presents a network architecture to interconnect VLSI1 neural network chips to build a distributed ANN(2) system. The architecture combines techniques from circuit switching and packet switching to provide two different service classes: isochronous connections and best-effort packet transfers. The isochronous connections are able to transport the axonal data of artificial neurons between VLSI ANN models that feature a speedup of multiples orders of magnitudes compared to biology. The connections use reserved bandwidth to provide loss-less transmissions as well as a low end-to-end delay with bounded jitter. Best-effort packet transfers use the remaining bandwidth for on-demand multi-purpose communication. The data forwarding is performed between synchronized instances of a dedicated switch architecture used at each network node. The switch is scalable in terms of port numbers and line speed. Its low complexity allows for an implementation within programmable logic or directly within a VLSI neural network chip. A reference implementation of the proposed network architecture is presented within an existing framework that hosts VLSI neural network chips operating at speedups of 10(4) to 10(5). The network architecture is further not limited to VLSI neural networks, but it can in principle be used in all network environments that require isochronous connections as well as packet processing.

关键词： VLSI neural networks quality of service isochronous communication crossbar switches AER

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Parallel distributed processing: Practical applications of neural networks in signal processing

Parallel distributed processing: Practical applications of n...

引用

COMSIG 88 - Southern African conference on Communications and Signal processing

作者： Schoonees, J.A. CSIR Div for Microelectron & Commun Technol Pretoria S Afr

ISBN: (纸本)0879427094

An introduction to artificial neural network models is presented, along with an overview of their practical application and potential applications in signal processing. Successful neural network implementations are described and their performances are compared to those of more traditional signal processing implementations. The Hopfield net, self-organizing feature maps, and the multilayer perceptron are reviewed. Implementation of neural nets in speech synthesis, speech recognition, target identification, image processing, pattern matching, error-correction coding, and neurocomputing are reported. Several ICs currently in production are briefly mentioned.

关键词： Signal processing

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