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

共 2988 条记录，以下是21-30 订阅

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Peer-to-Peer Learning Dynamics of Wide neural networks

Peer-to-Peer Learning Dynamics of Wide Neural Networks

引用

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

作者： Shreyas Chaudhari Srinivasa Pranav Emile Anand José M. F. Moura Electrical and Computer Engineering Carnegie Mellon University School of Computer Science Georgia Institute of Technology

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Peer-to-peer learning is an increasingly popular framework that enables beyond-5G distributed edge devices to collaboratively train deep neural networks in a privacy-preserving manner without the aid of a central server. neural network training algorithms for emerging environments, e.g., smart cities, have many design considerations that are difficult to tune in deployment settings – such as neural network architectures and hyperparameters. This presents a critical need for characterizing the training dynamics of distributed optimization algorithms used to train highly nonconvex neural networks in peer-to-peer learning environments. In this work, we provide an explicit characterization of the learning dynamics of wide neural networks trained using popular distributed gradient descent (DGD) algorithms. Our results leverage both recent advancements in neural tangent kernel (NTK) theory and extensive previous work on distributed learning and consensus. We validate our analytical results by accurately predicting the parameter and error dynamics of wide neural networks trained for classification tasks.

关键词： Training Computer aided instruction Distance learning Heuristic algorithms neural networks Signal processing algorithms Prediction algorithms Peer-to-peer computing Classification algorithms Optimization

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24th International conference on Embedded Computer Systems: Architectures, Modeling, and Simulation, SAMOS 2024

24th International Conference on Embedded Computer Systems: ...

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24th International conference on Embedded Computer Systems: Architectures, Modeling, and Simulation, SAMOS 2024

ISBN: (纸本)9783031783760

The proceedings contain 38 papers. The special focus in this conference is on Embedded Computer Systems: Architectures, Modeling, and Simulation. The topics include: QCEDA: Using Quantum Computers for EDA;Real-Time Linux on RISC-V: Long-Term Performance Analysis of PREEMPT_RT Patches;RV-VP2: Unlocking the Potential of RISC-V Packed-SIMD for Embedded processing;A Novel System Simulation Framework for HBM2 FPGA Platforms;ONNX-To-Hardware Design Flow for Adaptive neural-network Inference on FPGAs;efficient Post-training Augmentation for Adaptive Inference in Heterogeneous and distributed IoT Environments;pooling On-the-Go for NoC-Based Convolutional neural network Accelerator;Vitamin-V: Serverless Cloud Computing Porting on RISC-V;Design and Implementation of an Open Source OpenGL SC 2.0.1 Installable Client Driver and Offline Compiler;Plan Your Defense: A Comparative Analysis of Leakage Detection Methods on RISC-V Cores;iVault: Architectural Code Concealing Techniques to Protect Cryptographic Keys;I2DS: FPGA-Based Deep Learning Industrial Intrusion Detection System;ACRA: A Cutting-Edge Analytics Platform for Advanced Real-Time Corruption Risk Assessment and Investigation Prioritization;post Quantum Cryptography Research Lines in the Italian Center for Security and Rights in Cyberspace;advancing Future 5G/B5G Systems: The Int5Gent Approach;RISC-V Accelerators, Enablement and Applications for Automotive and Smart Home in the ISOLDE Project;PMDI: An AI-Enabled Ecosystem for Cooperative Urban Mobility;Open Source Software Randomisation Framework for Probabilistic WCET Prediction on Multicore CPUs, GPUs and Accelerators;a Hypervisor Based Platform for the Development and Verification of Reliable Software Applications.

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24th International conference on Embedded Computer Systems: Architectures, Modeling, and Simulation, SAMOS 2024

24th International Conference on Embedded Computer Systems: ...

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24th International conference on Embedded Computer Systems: Architectures, Modeling, and Simulation, SAMOS 2024

ISBN: (纸本)9783031783791

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13th International conference on Biomimetic and Biohybrid Systems, Living Machines 2024

13th International Conference on Biomimetic and Biohybrid Sy...

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13th International conference on Biomimetic and Biohybrid Systems, Living Machines 2024

ISBN: (纸本)9783031725968

The proceedings contain 31 papers. The special focus in this conference is on Biomimetic and Biohybrid Systems. The topics include: FlyWheel: A Robotic Platform for Modeling Fly Visual Behavior;a Coupled-Oscillator Model of Human Attachment Dynamics Evaluated in a Robot Dyadic Interaction;creating an Artificial No-Fly Zone with Sensory Disruptions;maximizing Robotic Limb Rigidity and Strain Sensing Capabilities Through Localized Kevlar Fiber Reinforcement;Flexible Strain Gauge Sensors as Real-Time Stretch Receptors for Use in Biomimetic BPA Muscle Applications;sensory Feedback Cancellation: Developing Resonator networks to Mimic A. leptorhynchu’s Cerebellar processing of Sensory Feedback;binocular Vision and Vector-Summation Based Integration of Bilateral Innate and Learned Visual Cues in Insect Navigation;bioinspired Magnetic Navigation for Exploring Celestial Bodies;bioinspired Navigation Based on distributed Sensing in the Leech Using Dynamic neural Fields;a Comparative Study of Reinforcement Learning and Insect-Inspired Visual Navigation Methods;erodium Awn’s Water Transport Insights for Controlled Swelling Agent Rearrangement in Anisotropic Structures;simulated Control of an Aquatic Serpentine Robot with Stable Heteroclinic Channels;a Comparison of Model-Free Controllers for Trajectory Tracking in a Plant-Inspired Soft Arm;pulse Modulation in Braided Pneumatic Actuators Mimics Contractile Behavior of Biological Muscles;design of a Rat Robotic Forelimb;cellular Plasticity Model for Bottom-Up Robotic Design;vertical Closure Constraint for Self-replicating Machines;mechanical Design of a Feline Robot for Dynamic Scaling Testing;moving Inward with Front Legs Improves Tripod Gaits for Crab-Like Robot Walking in Sand;encoding 3D Leg Kinematics Using Spatially-distributed, Population Coded network Model;analysis Pipeline for High-Dimensional Neuromechanical Model Improvement;sequence Generator network for Neuromechanical Control of Rat Hindlimbs.

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Execution Time Optimization for Pipeline Deep network Training on Multiple GPUs

Execution Time Optimization for Pipeline Deep Network Traini...

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Euromicro conference on Parallel, distributed and network-Based processing

作者： Bing-Jou Wu Ding-Yong Hong Pangfeng Liu Jan-Jan Wu Department of Computer Science and Information Engineering National Taiwan University Taipei Taiwan Institute of Information Science Academia Sinica Taipei Taiwan

ISBN: (数字)9798331524937

ISBN: (纸本)9798331524944

As neural network models become gigantic, they increasingly demand more time and memory for training. To meet these demands, advanced parallel computing techniques have become essential. Our research focuses on hybrid parallelism, an extension of pipeline parallelism. Pipeline parallelism splits the neural network into sub-networks distributed across a sequence of processing units, enabling simultaneous processing of different data segments on each device. Hybrid parallelism extends this concept by allocating multiple devices to each sub-network. Our research focuses on optimizing hybrid parallelism by improving how the model is partitioned and how computational devices are assigned. We address these issues by modeling the neural network as a directed acyclic graph of tensor operators, and then demonstrating that optimally partitioning this graph is NP-complete. Then, we propose a two-step approach. The first step is to determine a sequence of nodes. The second step is dynamic programming, which partitions the sequence to maintain balance across the assigned devices. In transforming the graph into a sequence, we explore two methods: one employs topological sorting, while the other clusters non-sequential subgraphs. We apply both methods and select the more effective one based on performance outcomes. We implement our algorithm and conduct experiments. The results show substantial enhancements in both the speed of partitioning and training throughput, with speedups reaching up to 23 in partitioning time and a 1.3 -fold increase in training throughput.

关键词： Training Directed acyclic graph Computational modeling neural networks Pipelines Computer architecture Parallel processing Throughput Dynamic programming Topology

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distributed Incentive Algorithm for Fine-Grained Offloading in Vehicular Ad Hoc networks 24th

Distributed Incentive Algorithm for Fine-Grained Offloading...

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24th International conference on Algorithms and Architectures for Parallel processing, ICA3PP 2024

作者： Wu, Junhong Wu, Yalan Huang, Jiale Wu, Jigang School of Computer Science and Technology Guangdong University of Technology Guangzhou China School of Integrated Circuits Guangdong University of Technology Guangzhou China

ISBN: (纸本)9789819615445

In vehicular ad hoc networks, few existing works on task offloading focus on co-offloading at intra-vehicle level and inter-vehicle level for deep neural network (DNN) inference. Moreover, they ignore the decentralized environment and selfishness of vehicles. This seriously limits the improvement of quality of services for DNN inference. To fill this gap, we formulate a fine-grained offloading problem considering two-level offloading and vehicle selfishness. To solve the problem, we propose a distributed algorithm based on coalition game to encourage vehicles to share their resources. Simulation results show that, the proposed algorithm outperforms the state-of-the-arts in terms of system payoff and running time of algorithms for most cases. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Consensus algorithm

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Automated Identification of Traffic Accidents in Images and Videos Employing Advanced Deep Learning Methods 25

Automated Identification of Traffic Accidents in Images and ...

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26th International conference on distributed Computing and networking, ICDCN 2025

作者： Kumari, Babli Dhir, Renu Chatterjee, Siddhartha Jain, Suchi Computer Science and Engineering Dr B R Ambedkar National Institute of Technology Punjab Jalandhar144011 India Computer Science and Engineering College of Engineering and Management Kolaghat West Bengal Purba Medinipur721171 India

ISBN: (纸本)9798400710629

An accident detection is essential since it ensures emergency response and raises the bar for road safety in general. In this paper, A CNN (Convolutional neural network) based collision recognition technique using the Tensorflow framework has been proposed. Deep learning has been used to automatically pick up information and identify important characteristics from pictures or videos taken by in-car cameras or roadside cameras. By utilizing a CNN architecture, model can effectively identify various accident-related patterns and distinguish them from normal traffic scenes. To train the CNN model, a large-scale dataset consisting of both accident and non-accident samples. The dataset is carefully labelled to ensure accurate ground truth annotations. During the training phase, data augmentation techniques has been employed to enhance the generalization ability of the model and mitigate overfitting. This technique has been used such as random crop, platooning and rotation to artificially enhance training diversity samples. For cropping, flipping and rotation of the images we have used image augmentation which is a function of ImageDataGenerator. After training, we evaluated the efficiency of our model on a separate test set, consisting of previously unseen accident and non-accident instances. Our model achieved 98% accuracy and robustness, demonstrating its effectiveness in accident detection. An OTP verification feature has been integrated to further enhance the system's functionality. This OTP verification confirms that emergency responders reach the accident location, adding a layer of security and ensuring follow-through in emergency responses. Overall, this study showcases the potential of deep learning techniques, specifically Convolutional neural networks, in addressing the crucial task of accident detection. This proposed method can integrate with the Computing Classification System (CCS), ensuring efficient categorization and real-time processing of data for acc

关键词： Convolutional neural networks

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24th International conference on Algorithms and Architectures for Parallel processing, ICA3PP 2024

24th International Conference on Algorithms and Architecture...

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24th International conference on Algorithms and Architectures for Parallel processing, ICA3PP 2024

ISBN: (纸本)9789819615476

The proceedings contain 131 papers. The special focus in this conference is on Algorithms and Architectures for Parallel processing. The topics include: MARO: Enabling Full MPI Automatic Refactoring in DSL-Based Programming Framework;SSC: An SRAM-Based Silence Computing Design for On-chip Memory;TP-BFT: A Faster Asynchronous BFT Consensus with Parallel Structure;LTP: A Lightweight On-Chip Temporary Prefetcher for Data-Dependent Memory Accesses;A neural network-Based PUF Protection Method Against Machine Learning Attack;Compression Format and Systolic Array Structure Co-design for Accelerating Sparse Matrix Multiplication in DNNs;multidimensional Intrinsic Identity Construction and Dynamic Seamless Authentication Schemes in IoT Environments;invisible Backdoor Attack with Image Contours Triggers;finestra: Multi-aggregator Swarm Learning for Gradient Leakage Defense;DIsFU: Protecting Innocent Clients in Federated Unlearning;multiple-Round Aggregation of Abstract Semantics for Secure Heterogeneous Federated Learning;dynamic Privacy Protection with Large Language Model in Social networks;a Dynamic Symmetric Searchable Encryption Scheme for Rapid Conjunctive Queries;a Data Watermark Scheme Base on Data Converted Bitmap for Data Trading;distributed Incentive Algorithm for Fine-Grained Offloading in Vehicular Ad Hoc networks;mitigating Over-Unlearning in Machine Unlearning with Synthetic Data Augmentation;AW-YOLOv9: Adverse Weather Conditions Adaptation for UAV Detection;efficient and Privacy-Preserving Ranking-Based Federated Learning;on-Chain Dynamic Policy Evaluation for Decentralized Access Control;DPG-FairFL: A Dual-Phase GAN-Based Defense Framework Against Image-Based Fairness Data Poisoning Attacks in Federated Learning.

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24th International conference on Algorithms and Architectures for Parallel processing, ICA3PP 2024

24th International Conference on Algorithms and Architecture...

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24th International conference on Algorithms and Architectures for Parallel processing, ICA3PP 2024

ISBN: (纸本)9789819615247

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