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Topological Dependencies in Deep Learning for Mobile Edge: distributed and Collaborative High-Speed Inference

Topological Dependencies in Deep Learning for Mobile Edge: D...

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Electronics and Renewable Systems (ICEARS), International conference on

作者： Yousef Methkal Abd Algani A Suresh Kumar Md. Abul Ala Walid Balu S Priya Velayutham A. Sasi Kumar Department of Mathematics The Arab Academic College for Education in Israel-Haifa Israel Department of Computer Science and Engineering Rathinam Technical Campus Coimbatore Tamil Nadu India Khulna University of Engineering & Technology (KUET) Khulna Bangladesh Department of Computer Science and Engineering Vivekanandha College of Engineering for Women Namakkal Tamil Nadu India Department of Computer Science and Engineering Paavai Engineering College Namakkal Tamil Nadu India Inurture Education Solutions Pvt Ltd Bangalore Department Of Cloud Technology & Data Science Institute Of Engineering & Technology Srinivas University Karnataka State India

Edge computing is now widely deployed across the globe. Although the Internet serves as the foundation for edge computing, the actual usefulness of this type of computing is seen when it is combined with the process of obtaining data from sensors and deriving relevant information from that data. It is predicted that in the not-too-distant era, most edge devices will be equipped with intelligent systems powered by deep learning. Unfortunately, in order to train, methods based on deep learning require a significant quantity of data of a high standard, and these methods are quite costly in terms of the amount of processing, memory, and power that they use. distributed deep neural networks, or DDNNs, are something suggested by using distributed computing hierarchies. The cloud, fog, and devices make up these tiers. Although a DDNN can facilitate the interpretation of a DNN in the cloud, it also allows for interpretation to be carried out quickly and precisely on the edges and on end devices by making use of shallow parts of the neural network. With the help of a scalable cloud-based infrastructure, a DDNN can expand both in terms of volume of its neural network and the number of users it serves around the world. For DNN applications, the distributed nature of DDNNs results in improvements to sensor fusion, fault tolerance in the system, and data privacy. In order to implement DDNN, first the portions of a DNN are mapped onto a dispersed computing structure. By learning both components together, the devices' need become limited for both connectivity and energy while the model provided the value of the selected features in the cloud. The final product includes provision for instinctive sensor fusion as well as fault tolerance that has been built directly into the system. This study demonstrates as a proof of concept that a DDNN may make use of the geographical variety of sensors to improve the accuracy of object detection and lower the cost of communication. The suggested

关键词： Deep learning Training Cloud computing Fault tolerance Image edge detection Computational modeling Fault tolerant systems

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Fog Enabled distributed Training Architecture for Federated Learning 9th

Fog Enabled Distributed Training Architecture for Federated ...

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9th International conference on Big Data Analytics (BDA)

作者： Kumar, Aditya Srirama, Satish Narayana Univ Hyderabad Sch Comp & Informat Sci Hyderabad 500046 Telangana India

ISBN: (纸本)9783030936198;9783030936204

The amount of data being produced at every epoch of second is increasing every moment. Various sensors, cameras and smart gadgets produce continuous data throughout its installation. processing and analyzing raw data at a cloud server faces several challenges such as bandwidth, congestion, latency, privacy and security. Fog computing brings computational resources closer to IoT that addresses some of these issues. These IoT devices have low computational capability, which is insufficient to train machine learning. Mining hidden patterns and inferential rules from continuously growing data is crucial for various applications. Due to growing privacy concerns, privacy preserving machine learning is another aspect that needs to be inculcated. In this paper, we have proposed a fog enabled distributed training architecture for machine learning tasks using resources constrained devices. The proposed architecture trains machine learning model on rapidly changing data using online learning. The network is inlined with privacy preserving federated learning training. Further, the learning capability of architecture is tested on a real world IIoT use case. We trained a neural network model for human position detection in IIoT setup on rapidly changing data.

关键词： Internet of Things Decentralized learning Fog computing

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Reconfigurable Event-Driven Spiking Neuromorphic Computing near High-Bandwidth Memory

Reconfigurable Event-Driven Spiking Neuromorphic Computing n...

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IEEE Biomedical Circuits and Systems (BIOCAS)

作者： Gopabandhu Hota Gwenevere Frank Keli Wang Abhinav Uppal Omowuyi Olajide Jeffrey Liu Shashank Bansal Kenneth Yoshimoto Qingbo Wang Stephen Deiss Gert Cauwenberghs Institute for Neural Computation UC San Diego La Jolla CA

This paper presents the hardware architecture and processing dataflow of a general-purpose reconfigurable, spike event-driven computing platform for running bio-inspired neural networks in real-time. Near-memory computing architecture and hardware-aware compiler optimization facilitate low-latency, distributed parallel execution of user-specified network models of arbitrary connection topology. The massively parallel processing architecture scales to implementation of very large networks through hierarchical address-event routing (HiAER) of spikes implementing long-range synaptic connectivity across the network. Large-size bio-inspired AI model capacity and high-performance neuronal network emulation in parallel hardware are enabled by virtue of memory-efficient network storage and execution dataflow optimized for sparse networks. Implemented on a high-end Field Programmable Gate Array (FPGA), the HiAER-Spike platform captures axonal and neuronal events in internal FPGA memories which, when activated, fetch synaptic connectivity from lookup tables stored in High Bandwidth Memory (HBM) 2.0 to update the neuron membrane state variables. Threshold crossing events trigger output spikes which in turn propagate as axonal activity through the network. The system streams input and output spike events over a PCIe 3.0 interface with the host station. A compiler toolchain generates the necessary FPGA and HBM hardware configuration for partitioning and mapping of the network topology and synaptic connectivity onto the HBM data structure. The memory organization and microarchitecture of the FPGA implementation supports 4M axons, 4M neurons, and 1B synapses, and leverages the nominal 450GBps throughput of HBM 2.0 for a throughput of 0.1 tera synaptic operations per second (TSynOps). The architecture efficiently handles both sparse connectivity and sparse activity for robust and low-latency event-driven inference for both edge and cloud computing, with examples demonstrating spike

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Study on the Construction of English ICAI Course Based on BP neural network Algorithm

Study on the Construction of English ICAI Course Based on BP...

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2021 International conference on Communications, Electronic Technology and Computer Engineering, CETCE 2021

作者： Wei, Yin Jilin Engineering Normal University Jilin China

Artificial neural network is also referred to as neural network or connection model, it's a model animal neural network, the mathematical model of distributed and information processing algorithm. Because of its good abstract classification, Has been used in all aspects of life. And in recent years, the traditional English teaching mode in colleges and universities has been gradually replaced by CAI new teaching methods, much progress has been made, Increasing resources to open schools and curricula, effectively promote the sharing of quality teaching resources, it promotes the improvement of students' English listening, speaking, reading, writing and translation. But the general problems of English CAI teaching have gradually emerged, for example, curriculum design does not pay enough attention to individual differences, online teaching lacks interaction between teachers and students, lack of effective supervision and assessment of learning results. At present, the student-centered teaching concept based on the class-flipping classroom and the "online and offline"mixed teaching model is becoming popular, which has had a great impact on the traditional foreign language teaching model. Designing English intelligent computer-assisted teaching (intelligent computer-assisted teaching) can intelligently meet the individual needs of students according to the learning effect, realize the interaction between teachers and students through human-computer interaction, and facilitate the management and evaluation of the teaching process by managers. This is referred to as ICAI. It is an urgent problem in English teaching, especially in the teaching of college English public courses. This paper designs an English ICAI language curriculum system based on BP neural network algorithm and SSH architecture. © Published under licence by IOP Publishing Ltd.

关键词： Computer aided instruction

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Sample drop detection for asynchronous devices distributed in space 28

Sample drop detection for asynchronous devices distributed i...

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28th European Signal processing conference, EUSIPCO 2020

作者： Raissi, Tina Pascual, Santiago Omologo, Maurizio Human Language Technology and Pattern Recognition RWTH Aachen University Aachen Germany Universitat Politècnica de Catalunya Barcelona Spain Trento Italy Dolby Laboratories Barcelona Spain

ISBN: (纸本)9789082797053

In many applications of multi-microphone multi-device processing, the synchronization among different input channels can be affected by the lack of a common clock and isolated drops of samples. In this work, we address the issue of sample drop detection in the context of a conversational speech scenario, recorded by a set of microphones distributed in space. The goal is to design a neural-based model that given a short window in the time domain, detects whether one or more devices have been subjected to a sample drop event. The candidate time windows are selected from a set of large time intervals, possibly including a sample drop, and by using a preprocessing step. The latter is based on the application of normalized cross-correlation between signals acquired by different devices. The architecture of the neural network relies on a CNN-LSTM encoder, followed by multi-head attention. The experiments are conducted using both artificial and real data. Our proposed approach obtained F1 score of 88% on an evaluation set extracted from the CHiME-5 corpus. A comparable performance was found in a larger set of experiments conducted on a set of multi-channel artificial scenes. © 2021 European Signal processing conference, EUSIPCO. All rights reserved.

关键词： Drops

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Development of the Railroad Switch Electric Drive Mathematical Model for the neural network

Development of the Railroad Switch Electric Drive Mathematic...

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International conference on Soft Computing and Measurements (SCM)

作者： Alena N. Iliushina Natal’Ja A. Shatilova Igor’ M. Novozhilov Saint-Petersburg College of Management and Commerce Saint Petersburg Russia Saint-Petersburg Mining University Saint Petersburg Russia Saint-Petersburg Electrotechnical University Saint Petersburg Russia

Market relations require final product quality improvement together with reducing its cost. Implementation of this task is possible only in case of the development of fundamentally new approaches to the analysis, processing, and issuance of management decisions for the automation of the technological process. In this paper, the authors analyze new approaches for measuring of physical quantities, as well as observe the application of the theory of systems with distributed parameters for identification of physical parameters of technological processes. The authors consider the possibility of using intelligent systems to identify these objects. As an object of study, the authors address the extractive industries of the mineral resource complex.

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Federated Deep Learning for Heterogeneous Edge Computing 20

Federated Deep Learning for Heterogeneous Edge Computing

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20th IEEE International conference on Machine Learning and Applications (ICMLA)

作者： Ahmed, Khandaker Mamun Imteaj, Ahmed Amini, M. Hadi Florida Int Univ Knight Fdn Sch Comp & Informat Sci Sustainabil Optimizat & Learning InterDependent N Miami FL 33199 USA

ISBN: (纸本)9781665443371

Nowadays, there is an ever-increasing deployment of intelligent edge devices, such as smartphones, wearable devices, and autonomous vehicles. It is enabled by the integration of advanced sensors with higher computing capabilities and widespread internet availability. These edge devices generate a vast amount of data that can be utilized for better inference. However, due to privacy concerns, communication overhead, processing delay, and security issues, traditional machine learning (ML) algorithms face challenges that work in a centralized fashion where all the available data is accumulated beforehand. Federated learning (FL) is a new distributed on-device learning method that generates a global model through the collaboration of edge devices without compromising data privacy. In this paper, we propose a federated transfer learning (FTL) model considering clients' heterogeneity in terms of their available computing resources and model architecture. We simulate the training performance of heterogeneous clients and observe that clients with sufficient resources require significantly lower computational time. In turn, the resource-constrained clients take notably higher computational time to accomplish a given task. Inspired by that, we design an FL model that constructs multiple global models based on the available resources of the clients and carries out a separate training process for each of the global models. We demonstrate the effectiveness of our proposed strategy by evaluating our FL model on CIFAR100 dataset. Our findings show that training time differs significantly among the heterogeneous clients and assigning multiple global models can notably improve convergence time.

关键词： Federated Learning model heterogeneity edge devices multiple global model model aggregation distributed deep learning deep neural networks Tensorfiow Federated recurrent neural network

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Gearbox fault diagnosis based on deep convolution generative adversarial network

Gearbox fault diagnosis based on deep convolution generative...

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International conference on Mechanical Engineering, Measurement Control, and Instrumentation 2021

作者： Jiang, Yiqiang He, Chen Sun, Li Wu, Bin School of Business Shanghai Dianji University Shanghai China

ISBN: (数字)9781510647299

ISBN: (纸本)9781510647282

The existing gearbox fault diagnosis models often need a lot of fault data and corresponding tags to complete the model training, but the actual fault data is often less and unevenly distributed. Aiming at the situation that the gearbox fault data is scarce and unevenly distributed, this paper proposes a fault diagnosis method based on the Deep Convolution Generative Adversarial network (DCGAN). First, the original vibration signals collected in the early stage are processed through data processing to form a sample data set and input into the DCGAN model for confrontation training, and virtual samples with real sample characteristics are generated to expand the sample data set. Then, the convolution neural network model is constructed to complete the fault diagnosis and get the results. The results show that the model trained by this method is more accurate when generating a large amount of data, and the diagnostic effect is significantly better. © 2021 SPIE.

关键词： Generative adversarial networks

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2021 3rd International conference on Smart Power and Internet Energy Systems, SPIES 2021

2021 3rd International Conference on Smart Power and Interne...

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3rd International conference on Smart Power and Internet Energy Systems, SPIES 2021

ISBN: (纸本)9780738146331

The proceedings contain 81 papers. The topics discussed include: speed control method of permanent magnet synchronous motor optimized by artificial bee colony algorithm;research on power factor correcting algorithm based on average detection;active power filter in linear systems for power factor correction;web processing control using backstepping and RBF neural networks;trajectory tracking control of manipulator based on particle swarm optimization fuzzy neural network;design and implementation of digital sampling for stability control device;and method for cluster partition of distributed generation considering flexible supply and demand balance and response speed.

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Time estimation for deep learning model’s inference in distributed processing units

Time estimation for deep learning model’s inference in dist...

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Latin America Congress on Computational Intelligence (LA-CCI)

作者： Ernesto Portugal Angel Ayala Francisco Cruz Bruno Fernandes Sergio Murilo Escola Politécnica de Pernambuco Universidade de Pernambuco Recife Brasil School of Computer Science and Engineering University of New South Wales Sydney Australia Escuela de Ingeniería Universidad Central de Chile Santiago Chile

One problem with cloud computing is that it may fail to meet the desired time limits for real-time applications. In this regard, fog computing paradigm has gained ground as it complements the cloud by providing nodes with processing and storage capabilities closer to the data generation level. However, this level of the architecture has limited resources, making it necessary to efficiently distribute the workload involved in applications, especially when employing deep learning models. One technique to achieve this is task offloading, which involves distributing inference tasks throughout the architecture. Nevertheless, it is also important to know the time required for these tasks to be carried out within the network in order to obtain the desired response. In this work, we propose a queue-based convolutional neural network that allows estimating the response time for a deep learning inference task. Preliminary results demonstrate a good fit to the behavior of the datasets used in the experiment.

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