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Hybrid Parallel Inference for Large Model on Heterogeneous Clusters for High throughput 29

Hybrid Parallel Inference for Large Model on Heterogeneous C...

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29th ieee international conference on Parallel and Distributed systems, ICPADS 2023

作者： Xu, Chaonong Kong, Weiming Liu, Min Zhang, Mingming Li, Chao Gong, Luqi Beijing Key Lab of Petroleum Data Mining Beijing China Institute of Intelligent Computing Zhejiang Lab Hangzhou China Bupt School of Computer Science National Pilot Software Engineering School Beijing China

ISBN: (纸本)9798350330717

In high-throughput intelligent computing scenarios, multi-device parallelism strategies based on data parallelism or pipeline parallelism have been extensively utilized to accelerate large deep neural network model inference. Data parallelism offers nearly linear improvement in inference speed, but it is limited by the memory capacity of a single device which constrains the model size. On the other hand, pipeline parallelism can support larger models, but the total communication of the activations among devices is high, which limits the improvement of the inference speed. To address the demand for efficient model inference in high-throughput heterogeneous scenarios, we proposes a hybrid parallelism strategy that combines data parallelism and pipeline parallelism. the strategy involves grouping heterogeneous device clusters and then employing inter-group data parallelism along with intra-group pipeline parallelism. Moreover, we propose an algorithm to find an optimal hybrid parallel inference strategy with maximum throughput. the control variables of the strategy includes the number of groups, group-device assignments and model partition ratios. Our experimental evaluation demonstrates that compared to PipeEdge, a pipeline parallel inference framework for heterogeneous cluster, our strategy can achieve 1.7× -3.4× acceleration in an 8-device heterogeneous cluster without loss of accuracy. © 2023 ieee.

关键词： data parallelism heterogeneous cluster hybrid parallelism model inference pipeline parallelism

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Design of Domino Logic-Based NOR Gate Circuit for Reduction of Charge Sharing 8th

Design of Domino Logic-Based NOR Gate Circuit for Reduction ...

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8th international conference on Microelectronics, Circuits, and systems, Micro 2021

作者： Srivastava, Saumya Shekhawat, Sangeeta Department of Electronics and Communication Engineering Chandigarh University Punjab Mohali Gharuan India Department of Electronics and Communication Engineering Amity University Rajasthan Jaipur India

ISBN: (纸本)9789819904112

In this paper, NAND gate circuit is designed by the help of domino logic circuit to improve the performance of NAND gate circuit. Domino logic circuit is introduced to resolve the problem of dynamic logic circuit. Domino logic circuit will help to remove the problem of charge sharing in dynamic logic circuit. this NAND gate circuit is the universal logic gate and by the help of this any digital circuit block can be easily design. In this modern era, all the things are digital. Here, power dissipation of the circuit is improved approx. 50–60% and delay of the circuit is reduced. Speed of the device will improve and the charge sharing problem is also resolved. All the simulations are carried out on electronic design automation (EDA) tool on LTSPICE software. Domino logic is one of the best technique when try to reduce power consumption as well as speed, delay of the device and delay of the circuit is reduced. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： computer circuits

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Natural Lighting System Using Fiber Optics for Energy Efficiency 8

Natural Lighting System Using Fiber Optics for Energy Effici...

引用

8th ieee international conference on engineering Technologies and Applied Sciences, ICETAS 2023

作者： Ibrahim, Babul Salam Soomro, Dur Muhammad Sundarajoo, Sharman Nordin, Zulhelmi Gulf University for Science & Technology Electrical and Computer Engineering Department Kuwait Universiti Tun Hussein Onn Malaysia Faculty of Electrical and Electronic Engineering Johor Malaysia

ISBN: (纸本)9798350327090

the sunlight's utilization as an illumination supply for lighting inside the building has expanded significantly since the 1900s. Lighting holds an integral part of the total electricity usage in a building. It is also one of the scopes with numerous ways to increase energy effectiveness by lowering energy usage. Hence, a natural lighting system is recommended to curtail electrical energy consumption for illumination throughout the daytime. this work aims to create a natural lighting system model to direct and address natural illuminance into challenging and complicated buildings. In this work, solar energy from the sun is conducted towards the fiber optics package's edge by utilizing a convex lens. this system aims to lower electrical-based lighting consumption during daytime illumination by transmitting the solar light over fiber optics. According to the sun's position, the system is sustained by an optic directional controller to guarantee the utmost connected illuminance. the control system would assure the highest illuminance accumulation also automatically turn on the electricity-based illumination if the acquired natural light did not satisfy the sufficient lux. the PIC16F877A microcontroller was used as a processor to power the tracking device engine. the percentage of the reduction in energy usage for daytime lighting was obtained. the obtained results show that this system will save energy during the day by 57%. therefore, this paper represents a notable improvement in optical fiber natural lighting systems' commercialization in energy-saving and cost reduction. © 2023 ieee.

关键词： Fiber optics

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software Vulnerability Detection Method Based on the Lexical and Syntax Analysis 9

Software Vulnerability Detection Method Based on the Lexical...

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9th Annual international conference on Network and Information systems for computers, ICNISC 2023

作者： Li, JiaYu Yao, Ye Zhu, Yian Mutitaba, Mutitaba Zhang, Yongkai Li, Hang Queen Mary University of London Engineering School Npu Northwestern Polytechnical University Xi'an City China Northwestern Polytechnical University School of Computer Science Xi'an City China

ISBN: (纸本)9798350306682

the paper presented and implemented a C-Scanner source code vulnerability detection tool, which can solve the buffer overflow problem. the main idea was to rewrite the source code of a C or C++, so that the resulting code contained the safe version of the old vulnerable functions, which may contain a buffer overflow security problem. If rewriting was not possible, due to some reasons, a warning was issued along with a hint to solve the problem if it was possible. C-Scanner in this paper took the C or C++ source code as input, and then it did a parsing process. Every time it encountered a vulnerable function call, it classified this function call. If this function call was belonging to a category of its interest, then it would rewrite this vulnerable function by a safe version, which prevented the buffer overflow vulnerability. © 2023 ieee.

关键词： Syntactics

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Animal Repellent System for Smart Farming using AI and Edge Computing 8

Animal Repellent System for Smart Farming using AI and Edge ...

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8th international conference on Advanced Computing and Communication systems, ICACCS 2022

作者： Sharmila F, Margret Amal Kumar, C.K. Varsha D, Angelin Sarayu, E. Sns College of Engineering Department of Cse Tamil Nadu Coimbatore India

ISBN: (纸本)9781665408165

Automation in agriculture is on the rise and leverages Deep Neural Networks (DNN) and IoT for the development of several controlling, monitoring, and tracking applications at a fine-grained level. Management of the ecosystem's relationships with external elements such as wildlife plays an increasingly important role in this rapidly changing setting. [1] there are different traditional approaches to addressing this problem - both lethal and non-lethal - that farmers use today to protect their crops from wild animal attacks. (e.g., scarecrow, chemical repellents, organic substances, mesh, or electric fences). [2] the traditional methods have nevertheless been associated with an array of environmental pollution effects on humans and ungulates, some of them were extremely expensive to maintain and unreliable, and some were ineffective. Using DCNN to detect and recognize animal species, and specific ultrasound emissions to repel them, we are developing a system that combines AI computer Vision for detection and repelling animals. [3] Once the camera is activated, the edge computing device executes DCNN software to identify the target, a message is sent to the Animal Repelling Module when it detects an animal, indicating the type of ultrasound that should be used, based on the category of the animal. © 2022 ieee.

关键词： Animals

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A New Robust Hybrid Active Noise Control System 8

A New Robust Hybrid Active Noise Control System

引用

8th international conference on Signal and Image Processing, ICSIP 2023

作者： Wang, Z. Xiao, Y. Ma, Y. Ma, L. Khorasani, K. Prefectural University of Hiroshima Dept. of Management & Information Systems Hiroshima734-8558 Japan Institute of Automation Jiangnan University Wuxi214122 China Concordia University Dept. of Electrical & Computer Engineering Montreal Canada

ISBN: (纸本)9798350397932

the conventional hybrid active noise control (ANC, HANC) may significantly degrade if the reference signal for the feedforward ANC (FFANC) subcontroller consists of not only a broadband component but also a low-frequency sinusoidal component that is uncorrelated or partially correlated with the narrowband noise component that is attenuated by the feedback ANC (FBANC) subcontroller. In this paper, a new robust HANC system is proposed to mitigate the performance degradation resulting from the low-frequency sinusoidal component. A band-pass filter bank (BPFB) is applied to the FFANC reference signal to separate the low-frequency sinusoidal component from the broadband one and each of them is fed to an FFANC subcontroller that solely focuses on a single noise component. A supporting filter takes the extracted broadband component and the residual error as its input and desired signal, respectively. the same BPFB is then applied to the supporting filter error in order to separate the remaining low-frequency sinusoidal component from the narrowband component that persists in the residual error. the use of the two BPFBs and the supporting filter allows the three HANC subcontrollers to operate practically independently, each taking care of one of the pre-processed noise components which are uncorrelated with each other irrespective of the relationship between the two original noise sources. Extensive simulations are provided to demonstrate the improved effectiveness and robust capability of the proposed HANC as compared to its counterpart, even in a case that the low-frequency sinusoidal component in the FFANC reference signal is partially corrected with the primary narrowband noise component that is targeted by the FBANC. © 2023 ieee.

关键词： Filter banks

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High Permittivity Ceramic- Polymer Composite Substrate for Wearable Antenna Applications 8

High Permittivity Ceramic- Polymer Composite Substrate for W...

引用

8th ieee Asia Pacific conference on Wireless and Mobile, APWiMob 2023

作者： Abu Bakar, Nur Hidayah Bakar, Aslina Abu Razali, Ahmad Rashidy Mozi, Aiza Mahyuni Ibrahim, Amirudin Radzali, Rosfariza Abd Rahim, Alhan Farhanah Faudzi, Najwa Mohd College of Engineering Universiti Teknologi Mara Electrical Engineering Studies Pulau Pinang Malaysia

ISBN: (纸本)9798350327861

Antenna systems must keep up with ever-increasing requirements and technical difficulties as wireless communications remain relevant. Microstrip patch antennas were chosen for their benefits. this study aimed to simulate various PDMS-Ceramic composites to simulate different permittivity's of potential ceramic polymer substrates and simulate a wearable antenna using the ceramic polymer substrate at 2.45 GHz. Dielectric ceramic-based substrates are excellent prospects for antenna downsizing. Since ceramic-polymer composites have a controllable dielectric constant and a low-loss tangent, they are very suitable for dielectric loading for potential wearable antenna. PDMSFerric Oxide (Fe2O3), PDMS-FR4, PDMS-Aluminum Oxide (Al2O3), PDMS-Barium Titanate (BaTiO3), PDMS-Strontium Titanate (SrTiO3), and PDMS-Mg-Ca-Ti (MCT) of ceramic-polymer have all been investigated in this proposed study. It is also simulated in two designs, which are uncut and cut antennas. then, the antenna simulation was performed using computer Simulation Technology (CST) software. All simulations of all prototypes are recorded, compared, and analysed. Both antenna designs have shown a good return loss performance for 2.45 GHz band applications and can be used for the same wireless communications application. the radiation pattern of the cut design patch shows omnidirectional properties. © 2023 ieee.

关键词： Substrates

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Unsupervised Semantic Representation Learning of Scientific Literature Based on Graph Attention Mechanism and Maximum Mutual Information 8

Unsupervised Semantic Representation Learning of Scientific ...

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8th ieee international conference on Cloud Computing and Intelligence systems, CCIS 2022

作者： Gao, Hongrui Li, Yawen Liang, Meiyu Guan, Zeli Beijing University of Posts and Telecommunications Beijing Key Laboratory of Intelligent Communication Software and Multimedia School of Computer Science National Pilot Software Engineering School Beijing100876 China Beijing University of Posts and Telecommunications School of Economics and Management Beijing100876 China

ISBN: (纸本)9781665477352

Since most scientific literature data are unlabeled, this makes unsupervised graph-based semantic representation learning crucial. therefore, an unsupervised semantic representation learning method of scientific literature based on graph attention mechanism and maximum mutual information (GAMMI) is proposed. By introducing a graph attention mechanism, the weighted summation of nearby node features makes the weights of adjacent node features entirely depend on the node features. Depending on the features of the nearby nodes, different weights can be applied to each node in the graph. In addition, an unsupervised graph contrastive learning strategy is proposed to solve the problem of being unlabeled and scalable on large-scale graphs. By comparing the mutual information between the positive and negative local node representations on the latent space and the global graph representation, the graph neural network can capture both local and global information. Experimental results demonstrate competitive performance on various node classification benchmarks, achieving good results and sometimes even surpassing the performance of supervised learning. © 2022 ieee.

关键词： Graph neural networks

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Adaptive Dual Channel Convolution Hypergraph Representation Learning for Technological Intellectual Property 8

Adaptive Dual Channel Convolution Hypergraph Representation ...

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8th ieee international conference on Cloud Computing and Intelligence systems, CCIS 2022

作者： Liu, Yuxin Li, Yawen Shao, Yingxia Guan, Zeli Beijing University of Posts and Telecommunications Beijing Key Laboratory of Intelligent Communication Software and Multimedia School of Computer Science National Pilot Software Engineering School Beijing100876 China Beijing University of Posts and Telecommunications School of Economics and Management Beijing100876 China

ISBN: (纸本)9781665477352

In the age of big data, the demand for hidden information mining in technological intellectual property (tech-IP) is increasing in discrete countries. Definitely, a considerable number of graph learning algorithms for technological intellectual property have been proposed. the goal is to model the technological intellectual property entities and their relationships through the graph structure and use the neural network algorithm to extract the hidden structure information in the graph. However, most of the existing graph learning algorithms merely focus on the information mining of binary relations in technological intellectual property, ignoring the higher-order information hidden in non-binary relations. therefore, a hypergraph neural network model based on dual channel convolution is proposed. For the hypergraph constructed from technological intellectual property data, the hypergraph channel and the line expanded graph channel of the hypergraph are used to learn the hypergraph, and the attention mechanism is introduced to adaptively fuse the output representations of the two channels. the proposed model outperforms the existing approaches on a variety of datasets. © 2022 ieee.

关键词： Convolution

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Embedding Representation of Academic Heterogeneous Information Networks Based on Federated Learning 8

Embedding Representation of Academic Heterogeneous Informati...

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8th ieee international conference on Cloud Computing and Intelligence systems, CCIS 2022

作者： Wang, Junfu Li, Yawen Liang, Meiyu Li, Ang Beijing University of Posts and Telecommunications Beijing Key Laboratory of Intelligent Communication Software and Multimedia School of Computer Science National Pilot Software Engineering School Beijing100876 China Beijing University of Posts and Telecommunications School of Economics and Management Beijing China

ISBN: (纸本)9781665477352

Existing studies for representation learning of homogeneous information networks cannot be applicable to academic heterogeneous information networks (HINs) with multi-type nodes and multi-relationships because of the lack of ability to issue heterogeneity. Meanwhile, due to the closeness and blocking of businesses among different enterprises, there is a serious phenomenon of data islands. To solve the above challenges, we proposed an academic heterogeneous information network embedding representation learning method based on federated learning (FedAHE), which utilizes node attention and meta path attention mechanism to learn low-dimensional, dense and real-valued vector representations while preserving the rich topological information and meta-path-based semantic information of nodes in network. Moreover, we combined federated learning with the representation learning of academic HINs and put forward a federal training mechanism based on dynamic weighted aggregation of parameters (FedDWA) to optimize the node embeddings of HINs. through sufficient experiments, the efficiency, accuracy and feasibility of our proposed framework are demonstrated. © 2022 ieee.

关键词： Semantics

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