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2nd International Conference on Electronics and Renewable Systems, ICEARS 2023

作者： Abd Algani, Yousef Methkal Kumar, A Suresh Ala Walid, Md. Abul Balu, S. Velayutham, Priya Sasi Kumar, A. The Arab Academic College for Education in Israel-Haifa Department of Mathematics Israel Rathinam Technical Campus Department of Computer Science and Engineering Tamil Nadu Coimbatore India Khulna9203 Bangladesh Vivekanandha College of Engineering for Women Department of Computer Science and Engineering Tamil Nadu Namakkal India Paavai Engineering College Department of Computer Science and Engineering Tamil Nadu Namakkal India Inurture Education Solutions Pvt Ltd Bangalore India Srinivas University Institute of Engineering & Technology Department of Cloud Technology & Data Science Karnataka State India

ISBN: (纸本)9798350346640

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： Edge computing

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Training Large-Vocabulary neural Language Models by Private Federated Learning for Resource-Constrained Devices 48

Training Large-Vocabulary Neural Language Models by Private ...

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48th IEEE International Conference on Acoustics, Speech and Signal processing, ICASSP 2023

作者： Xu, Mingbin Song, Congzheng Tian, Ye Agrawal, Neha Granqvist, Filip Van Dalen, Rogier Zhang, Xiao Argueta, Arturo Han, Shiyi Deng, Yaqiao Liu, Leo Walia, Anmol Jin, Alex Apple

ISBN: (纸本)9781728163277

Federated Learning (FL) is a technique to train models on distributed edge devices with local data samples. Differential Privacy (DP) can be applied with FL to provide a formal privacy guarantee for sensitive data on device. Our goal is to train a large neural network language model (NNLM) on compute-constrained devices while preserving privacy using FL and DP. However, the noise required to guarantee differential privacy increases as the model size grows, which often prevents convergence. We propose Partial Embedding Updates (PEU), a novel technique to reduce the impact of DP-noise by decreasing payload size. Furthermore, we adopt Low Rank Adaptation (LoRA) and Noise Contrastive Estimation (NCE) to reduce the memory demands of large models on compute-constrained devices. We demonstrate in simulation and with real devices that this combination of techniques makes it possible to train large-vocabulary language models while preserving accuracy and privacy. © 2023 IEEE.

关键词： Sensitive data

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GIM (Ghost In the Machine): A Coarse -Grained Reconfigurable Compute -In -Memory Platform for Exploring Machine -Learning Architectures

GIM (Ghost In the Machine): A Coarse -Grained Reconfigurable...

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1st International Conference on Smart Energy Systems and Artificial Intelligence (SESAI)

作者： Borowicz, Maya Ding, James Fan, Winnie Gao, Zhongqi Jackson, Davis Lu, Ares Rohlfsen, Sophia Simar, Ray Rice Univ Houston TX 77005 USA

ISBN: (纸本)9798350364613;9798350364606

Machine-learning (ML) algorithms are finding wide adoption across a rich spectrum of application domains with diverse requirements in terms of performance, power, and cost. These diverse requirements are making it necessary to explore a large space of ML architectures and reexamine fundamental computational structures, a process of exploration that is very expensive. To get around the costly computations associated with large data sets and long training times, there have been increasing investments in specialized fixed-function hardware. However, this specialized hardware is expensive and hard to generalize to address the spectrum of applications. For our experiments, we focus on a novel, highly parallel, superset ML architecture, and use it to test the capabilities of new coarse-grained FPGAs containing hundreds and thousands of DSP slices with dedicated local storage. These new coarse-grained architectures allow us to achieve ASIC-like clock rate and reductions in power while exploring novel and common ML architectures.

关键词： AMD architecture back propagation coarse grained compute in memory DSP FPGA machine learning neural network parallel processing Rice University RISC V Simulink systolic UltraScale Zync

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Cross-Attention Guided Group Aggregation network for Cropland Change Detection

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IEEE SENSORS JOURNAL 2023年第12期23卷 13680-13691页

作者： Xu, Chuan Ye, Zhaoyi Mei, Liye Shen, Sen Sun, Shaohua Wang, Ying Yang, Wei Hubei Univ Technol Sch Comp Sci Wuhan 430068 Peoples R China Naval Engn Univ Sch Weapon Engn Wuhan 430030 Peoples R China Wuchang Shouyi Univ Sch Informat Sci & Engn Wuhan 430032 Peoples R China

Cropland resources are essential for the provision of food production, which is one of the most fundamental needs of human life. Change detection (CD) technology enables the dynamic monitoring of high-resolution cropland resource images acquired through remote sensing satellite sensors. However, current CD methods are not capable of extracting meaningful change information from dense and continuously distributed cropland. In addition, the common feature fusion processing often results in information redundancy and the loss of key features. Therefore, we propose a cross-attention guided group aggregation network (CAGNet) to achieve effective cropland CD. Specifically, we adopt a cross-attention (CA) module to enhance the capability of extracting and characterizing the features of the changed region, reducing the influence of noise and pseudo-change on CD. To alleviate the loss of key information during the multiscale feature fusion process and thus improve the CD performance, we design a group aggregation (GA) module that gradually groups and aggregates the bitemporal features from coarse to fine. Finally, we use a fully convolutional network to obtain the detailed CD results. Furthermore, we demonstrate the effectiveness of knowledge transfer in the field of CD. It allows the models to obtain the underlying mechanisms and characterization capabilities of changed features on the building CD dataset in advance, which significantly improves the performance of various methods on the cropland CD dataset. The experimental results show that CAGNet's quantitative metrics results on the cropland dataset (CL-CD) outperform the other ten benchmarked methods, achieving an F1-score of 79.53%.

关键词： Feature extraction Sensors Remote sensing Data mining Knowledge transfer Production Convolutional neural networks Change detection (CD) cropland cross attention (CA) group aggregation (GA) remote sensing

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Memory Efficient Deep neural network Training 27th

Memory Efficient Deep Neural Network Training

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27th International European Conference on Parallel and distributed Computing (Euro-Par)

作者： Shilova, Alena Inria Bordeaux Sud Ouest Bordeaux France Univ Bordeaux Bordeaux France

ISBN: (纸本)9783031061561;9783031061554

Recently Artificial Intelligence (AI) has demonstrated a huge progress in solving complex problems such as image classification, text generation, translation... Its success is due to a development of hardware and algorithms making possible the emergence of Deep neural networks (DNNs). Such DNNs are composed of a number of operations from numerical linear algebra, whose order is defined with a Directed Acyclic Graph (DAG). These DNNs operate at the limit of the computational resources, and to go to deeper and more complex neural networks it is necessary either to design more powerful computational resources or optimize their usage. In our work, we address in particular the problem of lowering memory usage during the training of DNNs.

关键词： neural networks Rematerialization Offloading Model parallelism

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A Hybrid Approach combining ANN-based and Conventional Demapping in Communication for Efficient FPGA-Implementation 36

A Hybrid Approach combining ANN-based and Conventional Demap...

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36th IEEE International Parallel and distributed processing Symposium (IEEE IPDPS)

作者： Ney, Jonas Hammoud, Bilal Wehn, Norbert TU Kaiserslautern Kaiserslautern Germany

ISBN: (纸本)9781665497473

In communication systems, Autoencoder (AE) refers to the concept of replacing parts of the transmitter and receiver by artificial neural networks (ANNs) to train the system end-to-end over a channel model. This approach aims to improve communication performance, especially for varying channel conditions, with the cost of high computational complexity for training and inference. Field-programmable gate arrays (FPGAs) have been shown to be a suitable platform for energy-efficient ANN implementation. However, the high number of operations and the large model size of ANNs limit the performance on the resource-constrained devices, which is critical for low-latency and high-throughput communication systems. To tackle his challenge, we propose a novel approach for efficient ANN-based demapping on FPGAs, which combines the adaptability of the AE with the efficiency of conventional demapping algorithms. After adaption to channel conditions, the channel characteristics, implicitly learned by the ANN, are extracted to enable the use of optimized conventional demapping algorithms for inference. We validate the hardware efficiency of our approach by providing FPGA implementation results and by comparing the communication performance to that of conventional systems. Our work opens a door for the practical application of ANN-based communication algorithms on FPGAs.

关键词： Training Performance evaluation Runtime Transmitters Artificial neural networks Receivers Inference algorithms

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Research on Performance of Public Welfare Crowdfunding Based on neural network Intelligent Model

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SCIENTIFIC PROGRAMMING 2022年第1期2022卷

作者： Xia, Zhenqin Iowa State Univ Business Sch Ames IA 50010 USA

From the appearance of entrepreneurial narratives, it communicates the movement of innovatory suggestion and entrepreneurial madness on revenue deed in technology crowdfunding, outsearch the immediate percussion of innovatory hint on financier act, and the moderating sign of entrepreneurial emotion on the relationship between novelty and resources exploit. Through the mode of text-book analysis, the genuine technology design in JD crowdfunding was interested in bearing empiric trial of the mold. neural networks have large-scale parallel, distributed storage and processing, self-organization, self-adaptation, and self-learning capabilities and are especially suitable for dealing with inaccurate and ambiguous information processing problems that need to consider many factors and conditions at the same time. The empiric arise shows that forward-looking neologism queue indubitably moves financier completion, and primitive neology suggestion negatively pretends resources accomplishment;pierce People's entrepreneurial emotion intensify the dogmatic outcome of incremental neology humor on financier work, but has no momentous regulatory performance on the relationship between fundamental novation cue up and revenue exploit. On this base, a nerve plexure pattern is further fabricated for equal analysis. The characteristic efficiency of retrogradation example and nerval meshwork fashion is amended. In addition, based on the sample data of the Dahuotou fair crowdfunding platform, the indicators of the extended resource evaluation are judged based on the video revenue duration of the design scheme, scheme dynamics, extended scoring, non-hidden technology, and employee inclusiveness. There is an interaction manifestation between video and operant allowance data;the penetration sign of the two logical recession molds is emended than that of the multilayer McCulloch-Pitts neuron mold and the divergent base duty design.

关键词： Molds

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Hierarchical Collaborative Resource Scheduling in Industrial Internet of Things based on Graph neural networks and Deep Reinforcement Learning 29

Hierarchical Collaborative Resource Scheduling in Industrial...

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29th IEEE International Conference on Parallel and distributed Systems, ICPADS 2023

作者： Meng, Qifeng Luo, Zihui Zheng, Xiaolong Liu, Liang Ma, Huadong Beijing University of Posts and Telecommunications Beijing100876 China

ISBN: (纸本)9798350330717

The hierarchical cooperative resource scheduling architecture provides a promising direction for efficient collaborative processing of edge computing under the dynamic and intricate landscape of the Industrial Internet of Things (IIoT). However, existing scheduling algorithms often struggle to effectively capture the intricate information features inherent in hierarchical and collaborative domains, leading to suboptimal solutions. To tackle this challenge, we introduce a novel hierarchical cooperative resource scheduling framework based on Graph neural networks (GNN) and Deep Reinforcement Learning (DRL). We first leverage hierarchical GNN to facilitate seamless information exchange among internal nodes and adjacent nodes between layers in the hierarchical structure and transform it into node embeddings. These meticulously designed embeddings are then input into the policy model of DRL for the iterative learning process to generate higher-quality solutions by leveraging global feature information. Experiment results unequivocally demonstrate the superiority of our approach over baselines in terms of scheduling performance. Furthermore, our model exhibits robust generalization capabilities across various scenarios. © 2023 IEEE.

关键词： Deep Reinforcement Learning Edge Computing Graph neural network Hierarchical Collaborative Industrial Internet of Things

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Deep Learning-Based Detection of Seedling Development from Indoor to Outdoor 28th

Deep Learning-Based Detection of Seedling Development from I...

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28th International Conference on Systems, Signals and Image processing (IWSSIP)

作者： Garbouge, Hadhami Rasti, Pejman Rousseau, David Univ Angers UMR INRAe IRHS LARIS 62 Ave Notre Dame du Lac F-49000 Angers France ESAIP Dept Comp Sci Angers France

ISBN: (纸本)9783030968786;9783030968779

Monitoring plant growth with computer vision is an important topic in plant science. This monitoring can be challenging when plants are located in outdoor conditions due to light variations and other noises. On other hand, there is a lack of annotated datasets available for such outdoor environments to train machine learning algorithms while indoor similar datasets may be more easily available. In this communication, we investigate, for the first time to the best of our knowledge in plant imaging, how to take benefit from model trained in fully controlled environment to build model for an outdoor environment. This is illustrated with a use case recently published for indoor conditions that we revisit and extend. We compare various spatial and spatio-temporal neural network architectures including long-short term memory convolutional neural network, time distributed convolutional neural network and transformer. While the spatio-temporal architectures outperform the spatial one in indoor conditions, the temporal information appears to be degraded by the presence of shadows due to the variation of light in outdoor conditions. We introduce a specific data augmentation and transfer learning approach which enables to reach a performance of 91% of good classifications with very limited effort of annotation.

关键词： Plant phenotyping Deep learning Transfer learning Data augmentation CNN-LSTM Time distributed deep learning Transformers

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ANN based Faulty Phase Identification and Fault Resistance Estimation in Inverter Interfaced Grid Connected Microgrids 3

ANN based Faulty Phase Identification and Fault Resistance E...

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3rd IEEE Applied Signal processing Conference, ASPCON 2023

作者： Debnath, Anushka Roy, Subhamita National Institute of Technology Department of Computer Science and Engineering Durgapur India Jadavpur University Department of Electrical Engineering Kolkata India

ISBN: (纸本)9798350325881

Fault detection is a challenging task in inverter interfaced microgrids as fault current in inverter interfaced distributed generator (IIDG) is limited. The different control strategies adopted by IIDGs make the fault detection more complex. For quick restoration of normal supply, faulty phase identification is essential. The root cause of the fault can be assessed from the estimation of fault resistance. This paper utilizes artificial neural network (ANN) to identify the faulty phase in feeders and sub-feeders in the microgrid acquiring current signals from the substation. Further, fault resistance has been estimated utilizing ANN. Power spectral density (PSD) of the three phase current signals at the substation calculated utilizing discrete wavelet transform (DWT) has been fed to ANN model to classify faults and estimate fault resistance. Only half cycle of the current signal after fault is required and the simulation results establishes the efficacy of the proposed technique. © 2023 IEEE.

关键词： neural networks

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