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Synergy Exploration in Deploying Convolutional Neural networks Across distributed Neuromorphic System

Synergy Exploration in Deploying Convolutional Neural Networ...

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IEEE Instrumentation and Measurement Technology conference

作者： Bo Gong Jiang Wang Siyuan Chang Weitong Liu Tong Li School of Electrical and Information Engineering Tianjin University Tianjin China

ISBN: (数字)9798350380903

ISBN: (纸本)9798350380910

Convolutional Neural networks (CNNs) are an essential part of distributed neuromorphic systems and require an efficient strategy for deployment. We proposed a synergetic distributed mapping mechanism for CNNs to address the problem of parallel computing and resource optimization of CNNs in neuromorphic systems. A dataset recording soccer positions containing 4000 grayscale images of 105*105 pixels was built. A CNN model was constructed based on it and deployed in the developed neuromorphic system BrainS. As tested in the robotic platform, the model has an accuracy of 95.2% in the test set and can accurately control the movement of the omnidirectional robot. The Root Mean Square Error (NMSE) between BrainS and GPU for all layers during the forward computation of the model was $2.00 \times {10^{ - 6}}$ . By adjusting the number of guard bits in the computing process, the system's time consumption can be reduced. The proposed method in this paper provides a practical and effective solution for CNN deployment in neuromorphic systems while guaranteeing accuracy.

关键词： Accuracy Neuromorphics Computational modeling Graphics processing units Gray-scale Brain modeling Recording

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Fine-grained Task Scheduling Based on Graph Neural Network and Federated Learning in Vehicle Edge computing

Fine-grained Task Scheduling Based on Graph Neural Network a...

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international conference on parallel and distributed Systems (ICPADS)

作者： Gaowei Zhang Junping Song Yahui Hu Pengfei Fan Chong Li Xu Zhou Computer Network Information Center Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China China University of Mining and Technology Beijing China

Vehicle Edge computing (VEC) has emerged as an efficacious paradigm that supports real-time, computation-intensive vehicular applications. However, due to the highly dynamic nature of computing node topology, existing scheduling algorithms need to more effectively apprehend the characteristics of fine-grained task topologies and network topologies. Moreover, they require significant communication overhead and training costs, making them inadequate for fine-grained task scheduling in vehicular networks. In response, our research explores fine-grained task scheduling issues within VEC scenarios, proposing a scheduling algorithm based on Graph Neural networks and Federated Learning (FL-GNN). This algorithm maintains a global scheduling model that periodically aggregates local scheduling models deployed on Roadside Units (RSUs) and high-performance vehicles. Furthermore, to enhance the model’s ability to perceive topology and expedite the convergence rate, we incorporate a graph neural network layer in each local model to preprocess the raw state of the VEC environment. Lastly, we construct a simulation platform and implement multiple competitive solutions, demonstrating the superiority of the FL-GNN algorithm in aspects such as reducing the average task delay, balancing the load, and improving the task scheduling success rate.

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A Comprehensive Study of Energy Efficiency in Cloud computing Using Optimization Algorithms

A Comprehensive Study of Energy Efficiency in Cloud Computin...

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Data Science and Network Security (ICDSNS), international conference on

作者： Mohammed Ziaur Rahman Anandaraj Shanthi Pichandi School of CSE & IS Presidency University Bangalore India

ISBN: (数字)9798350373110

ISBN: (纸本)9798350373127

Cloud computing (CC) is a fundamental technology for distributed networks, enabling the execution of programs across multiple networked computers simultaneously. The major challenge in cloud computing (CC) is optimizing energy utilization, which involves improving the ratio of power consumed by the entire facility to the power consumed, specifically by utilizing IT resources. Energy efficiency in cloud computing includes various techniques like Load balancing, task scheduling, and Resource Allocation (RA). In CC, load balancing is a process which distributes the workloads and traffic to make sure that no single server or system is overloaded or under-loaded. Task scheduling in cloud computing is utilized to allocate tasks to the best suitable resource for execution. The RA in CC is process of allocating available resources to required cloud applications over internet and starving the services when allocation is not controlled correctly. In this study, various methodologies like Modified Multi-Objective Harris Hawk Optimization (MMHHO), Deep Reinforcement Learning with parallel Particle Swarm Optimization (DRLPPSO), Convolutional Neural Network (CNN) - Modified Particle Swarm Optimization (MPSO), and so on, are used to ensure energy efficiency in cloud computing. These algorithms attain high resource utilization and decreases the response time and cost of maintenance.

关键词： Cloud computing Processor scheduling Load management Energy efficiency Scheduling Resource management Convolutional neural networks Time factors Servers Optimization

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Hetero-Vis: A Framework for Latency Optimized Heterogeneous Deployment of Convolutional Neural networks 28th

Hetero-Vis: A Framework for Latency Optimized Heterogeneous...

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28th international European conference on parallel and distributed computing , Euro-Par 2022

作者： Sumeet, Nupur Rawat, Karan Nambiar, Manoj Singhal, Rekha Tata Consultancy Services Research Mumbai400607 India

ISBN: (纸本)9783031312083

Convolutional Neural Network (CNN) models often comprise multiple layers varying in compute requirements. For deployment, a number of hardware accelerators are available that have subtle differences in compute architectures within the same family of platforms. A component (a set of layers) of a CNN model may perceive different performance in different compute architectures. Optimal mapping of the components of a CNN model across a given heterogeneous architecture can leverage underlying different compute architectures to deliver minimum inference latency3. In this paper, we present an optimal partitioning approach to map a CNN model across heterogeneous architectures by leveraging a repository of performance-measurement-benchmark (PerfLib) for different accelerators. Our proposed framework, Hetero-vis, decides optimal partitions and mapping of a CNN network across different accelerators to minimize the inference latency. Our experiments reveal up to 1.43 × better performance with grouped layer deployment of CNN models on heterogeneous hardware compared to the entire model deployed on a single accelerator.1 Inference latency and latency terms are used interchangeably in the rest of the paper. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Neural network models

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Pipelined Model parallelism: Complexity Results and Memory Considerations 27th

Pipelined Model Parallelism: Complexity Results and Memory C...

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27th international European conference on parallel and distributed computing (Euro-Par)

作者： Beaumont, Olivier Eyraud-Dubois, Lionel Shilova, Alena Univ Bordeaux Inria Bordeaux Sud Ouest Bordeaux France

ISBN: (纸本)9783030856656;9783030856649

The training phase in Deep Neural networks has become an important source of computing resource usage and the resulting volume of computation makes it crucial to perform efficiently on parallel architectures. Data parallelism is the most widely used method, but it requires to replicate the network weights on all processors, and to perform collective communications of the network weights. In this context, model parallelism is an attractive alternative, in which the different layers of the network are distributed over the computing processors. Indeed, it is expected to better distribute weights (to cope with memory problems) and it eliminates the need for large collective communications since only forward activations are communicated. However, to be efficient, it must be combined with pipelining, which in turn induces new memory costs. In this paper, our goal is to formalize pipelined model parallelism as a scheduling problem, to establish its complexity, and to analyze the importance of the assumptions of contiguity and 1-periodicity, implicitly made in practical solutions such as PipeDream.

关键词： Deep neural networks

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19th IFIP WG 10.3 international conference on Network and parallel computing, NPC 2022

19th IFIP WG 10.3 International Conference on Network and Pa...

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19th IFIP WG 10.3 international conference on Network and parallel computing, NPC 2022

ISBN: (纸本)9783031213946

The proceedings contain 31 papers. The special focus in this conference is on Network and parallel computing. The topics include: Adjust: An Online Resource Adjustment Framework for Microservice Programs;cloud-Native Server Consolidation for Energy-Efficient FaaS Deployment;neuProMa: A Toolchain for Mapping Large-Scale Spiking Convolutional Neural networks onto Neuromorphic Processor;Multi-clusters: An Efficient Design Paradigm of NN Accelerator Architecture Based on FPGA;TrainFlow: A Lightweight, Programmable ML Training Framework via Serverless Paradigm;DRP:Discrete Rank Pruning for Neural Network;transMigrator: A Transformer-Based Predictive Page Migration Mechanism for Heterogeneous Memory;Hardware Acceleration for 1D-CNN Based Real-Time Edge computing;DLC: An Optimization Framework for Full-State Quantum Simulation;Optimizing Winograd Convolution on GPUs via Partial Kernel Fusion;Approximation Algorithms for Reliability-Aware Maximum VoI on AUV-Aided Data Collections;CCSBD: A Cost Control System Based on Blockchain and DRG Mechanism;Number of UAVs and Mission Completion Time Minimization in Multi-UAV-Enabled IoT networks;a Spatial-Temporal Similarity-Based Cooperative Surveillance Framework by Edge;A Progressive Transmission Method of Cloud Point Data for HD Map in Autonomous Driving;IMRSim: A Disk Simulator for Interlaced Magnetic Recording Technology;Alleviating Performance Interference Through Intra-Queue I/O Isolation for NVMe-over-Fabrics;WALOR: Workload-Driven Adaptive Layout Optimization of Raft Groups for Heterogeneous distributed Key-Value Stores;Efficient Data Placement for Zoned Namespaces (ZNS) SSDs;SchedP: I/O-aware Job Scheduling in Large-Scale Production HPC Systems;adaptive Low-Cost Loop Expansion for Modulo Scheduling;SpacKV: A Pmem-Aware Key-Value Separation Store Based on LSM-Tree;Consistent and Efficient Batch Operations for NoSQL Databases with Hybrid Timestamp;SADD: A Novel Systolic Array Accelerator with Dynamic Dataflow for Sparse GEMM i

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Incremental Conductance INC-MPPT Based Optimization of Solar Energy Harvesting System for WSN Nodes

Incremental Conductance INC-MPPT Based Optimization of Solar...

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international conference on parallel distributed and Grid computing (PDGC)

作者： Sunil Kumar Saurav Chandra Navin Prakash Anil Kumar Anju Mishra Department of Information Technology Ajay Kumar Garg Engineering College Ghaziabad Uttar Pradesh India Department of CSE KIET Group of Institutions Ghaziabad Uttar Pradesh India Department of CSE (AI) IIMT College of Engineering Greater Noida Uttar Pradesh India Department of CSE Roorkee Institute of Technology Roorkee Uttrakhand India

ISBN: (数字)9798331521349

ISBN: (纸本)9798331521356

Solar energy is the source of energy that is both environmentally friendly and sustainable. Recently, research in the Internet of Things has focused on the application of wireless sensor networks (WSN) using solar energy collecting techniques. The energy stored in the WSN node battery is negligible, and depending on the working duty cycle, it may only maintain its capacity for a few days. Our research presents a new SEH approach that we propose for use with energy-constrained wireless sensor network nodes. Solar Energy Harvesting Wireless Sensor Network (SEHWSN) nodes are typically able to continue operating for a considerable amount of time. In the past, the process of collecting solar energy was established by employing the method of P&O Maximum Power Point Tracking (MPPT), which produced very favorable outcomes because of its effectiveness. Nevertheless, the effects that INC (Incremental Conductance) produces are superior to those that P&O produces. For the purpose of applying SEHWSN, we are utilizing the INC-MPPT technique, and we are comparing it to the methodology of P&O-MPPT. The P&O-MPPT has a total efficiency of 89.80%, while the INC-MPPT has 93.37%.

关键词： Maximum power point trackers Wireless sensor networks Solar energy DC-DC power converters Grid computing Batteries Internet of Things Optimization

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A Novel Approach to Ensure Security and Privacy While Using QR Code Scanning in Business Applications 7

A Novel Approach to Ensure Security and Privacy While Using ...

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7th international conference on parallel, distributed and Grid computing, PDGC 2022

作者： Bhamidipati, Venkateswara Sarma Sai, W.V.S. Raghavendra Sap Labs India Pvt. Ltd. ERP4SME Central Operations Karnataka Bangalore India Sap Labs India Pvt. Ltd. Business Networks Engineering Karnataka Bangalore India

ISBN: (纸本)9781665454018

One of many challenges created by COVID-19 pandemic is to reduce need of contact. Quick Response (QR) codes offered a readily available solution to this challenge with offer to support contact less processes. Wide adaption of smart mobile devices like smart phones and tablets and huge number of mobile applications available in the respective application stores, which support QR code scanning acted as a catalyst in rapid adaption of QR codes to support contact less processes. Support of QR code-based processing rapidly increased during the pandemic, penetrated all processes like sales and marketing, authentication, and digital payments to name some. On one hand, this served the cause in terms of reducing contact, on other hand, factors like rapid adaption and using it in smart mobile devices, which are existing to cater to the larger purpose of human usage, scanning QR codes was not in that list to start with is bringing in the series of security issues which can arise starting from the human factor, software, misuse and hacking factors. This paper focuses on the QR code processes, differences in terms of security while using a smart device for QR codes when compared to the rugged device-based barcode scanners, the kind of security issues such process can encounter while using smart devises for QR code scanning, factors that must be considered by the applications development as well as the consumers of such functionality and the way to ensure security of consumers of such functionality. © 2022 IEEE.

关键词： COVID-19

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Artificial Intelligence in Smart Mobile Communication Systems: A Review

Artificial Intelligence in Smart Mobile Communication System...

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IEEE international conference on Emerging & Sustainable Technologies for Power & ICT in a Developing Society (NIGERCON)

作者： Adebajo-Akinpelu Victor O Moses Oluwafemi Onibonoje Ayodeji Olalekan Salau Department of Electrical/Electronics and Computer Engineering Afe Babalola Univerisity Ado-Ekiti Ado-Ekiti Nigeria Dept of Electrical/Electronics & Computer Engineering Afe Babalola University Ado Ekiti Nigeria

ISBN: (数字)9798331542559

ISBN: (纸本)9798331542566

Communication and information service systems are undergoing rapid advancements driven by the evolution of telecommunications and computer technology. The convergence of these fields has led to significant growth in digital networks, which has spurred extensive research and exploration. However, optimizing the use of these networks with minimal additional costs—while integrating features such as video conferencing, email services, and remote database access— remains a substantial technological challenge. This study addresses the limitations of current mobile communication networks by exploring potential enhancements through the application of smart computing solutions, particularly artificial intelligence (AI). The research methodology involves a comprehensive review of existing mobile communication technologies and their integration with AI. This includes examining advancements in AI and machine learning (ML) as applied to mobile networks, with a focus on improving performance, flexibility, and automation. The results of the review indicate that AI and ML have significant potential to enhance network operations. By leveraging vast data generated within 5G systems, these technologies can optimize resource utilization, improve service quality, and support diverse emerging applications. Despite these advancements, challenges such as reliability, speed trade-offs, complexity, privacy, and security persist. The findings highlight the need for further research in AI, including deep neural network acceleration, parallel computing, cloud computing, and distributed deep learning systems. Such research is crucial for enabling mobile communication networks to achieve higher throughput and ultra-low latency. Conclusively, while AI-enabled communication systems offer promising solutions to many challenges, simplifying AI implementation is essential for practical deployment. This will ensure that future communication networks can effectively meet the growing demands and evolving

关键词： Deep learning Privacy Reviews 5G mobile communication parallel processing Throughput Complexity theory Security Resource management Artificial intelligence

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Leveraging Edge computing and Privacy-Enhanced Prediction Sharing for Urban Traffic Forecasting

Leveraging Edge Computing and Privacy-Enhanced Prediction Sh...

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international conference on parallel and distributed Systems (ICPADS)

作者： Yiping Wang Chunyang Ye Hui Zhou School of Comupter Science and Technology Hainan University Haikou China

In today’s urban transportation landscape, the accurate and timely prediction of traffic conditions holds immense significance for optimizing traffic flow and resource allocation. However, conventional centralized methods for traffic prediction come with the drawback of data collection from various roads, a process that can introduce latency and privacy concerns. To address these pressing challenges, our innovative approach seamlessly integrates road spatiotemporal data, edge computing, and privacy protection inspired by federated learning. We utilize Long Short-Term Memory (LSTM) networks to make real-time predictions of vehicle speeds on individual road segments. Furthermore, we introduce Graph Neural networks (GNN) to effectively amalgamate predictive insights from the target road and its neighboring roads, effectively capturing intricate spatial relationships within traffic data. Our approach excels in reducing data requirements and alleviating computational burdens, enabling straightforward deployment on edge servers tailored to individual road segments. Most importantly, it prioritizes privacy by circumventing the exchange of raw data, instead opting for the selective sharing of predictions between segments. Our rigorous experimental results demonstrate that our approach outperforms traditional federated averaging algorithms, underscoring its remarkable security attributes while achieving superior predictive accuracy.

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