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Exploiting Fine-Grained Structured Pruning for Efficient Inference on CNN Model 29

Exploiting Fine-Grained Structured Pruning for Efficient Inf...

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

作者： Wu, Cheng-Hung Hong, Ding-Yong Liu, Pangfeng Wu, Jan-Jan National Taiwan University Department of Computer Science and Information Engineering Taiwan Institute of Information Science Academia Sinica Taiwan

ISBN: (纸本)9798350330717

Weight pruning is a technique to remove redundant or unimportant weights from the network. It can help reduce the size and computational cost of neural networks while preserving their accuracy. In this paper, we aim to design efficient CNN models with N:M pruning on the CPU. We propose a dynamic programming algorithm to find a good sparsity ratio for every layer under a total time budget based on the execution times and L1 norm of layers. After deciding the sparsity ratio of each layer, we leverage the auto-tuner of the TVM compiler to search for an optimization schedule of the pruned convolution to accelerate fine-grained pruned models. Experimental results show that our scheme can achieve 0.35% accuracy improvement and a 1.55× speedup on VGG-16 with ImageNet than the dense model. © 2023 IEEE.

关键词： deep neural network weight pruning

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13th International conference on Frontier Computing, FC 2023

13th International Conference on Frontier Computing, FC 2023

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13th International conference on Frontier Computing, FC 2023

ISBN: (纸本)9789819992980

The proceedings contain 180 papers. The special focus in this conference is on Frontier Computing. The topics include: Optimization of English Machine Translation Model Based on neural network;exploration on Text Detection Optimization Algorithm Based on neural network Technology;CO2 Emission Prediction of Vehicle Fuel Consumption Based on EMD-LSTM;application of Data Fusion Algorithms in the Data processing of Intelligent Greenhouses;intelligent Film and Television Communication Optical Technology Based on network New Media;design of Heterogeneous Database Encryption and Decryption System Based on Data Mining;research and Implementation of Music Recommendation System Based on Particle Swarm Algorithm;design of High-Speed Multi-channel Data Transmission System Based on Single-Chip Microcomputer;application of distributed Database System in Financial Management;Research on the Development of Japanese MOOC System Based on BP neural Algorithm;research on Default Risk Prediction of Listed Companies’ Green Credit Based on Deep Learning Algorithm;improvement of Cloud Platform Utilization Based on Evaluation and Optimization of Computing Resource Runtime Surplus Capacity;design and Implementation of Operation and Maintenance Monitoring Platform Based on Data Mining Algorithm;civil Aviation Logistics Transportation Route Optimization Algorithm Based on A+ Algorithm;design of Interactive Japanese Translation System Based on Feature Extraction Algorithm;a Preliminary Study on Low-Carbon City Planning Methods Supported by the Cluster Optimization Algorithm;object Detection Algorithms in Embedded Systems and their Applications;Application of Computer VR Technology in Digital Media System Design;discuss the Technical Application of Cloud Data Center and Cloud Management Platform;Design of ARM-Based Program Automatic Shelling Data Acquisition System.

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An Overview of Different Approaches used for processing Satellite Images

An Overview of Different Approaches used for Processing Sate...

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2024 International conference on distributed Systems, Computer networks and Cybersecurity, ICDSCNC 2024

作者： Chethana, V. Rashmi, S. Visvesveraya Technological University Dayananda Sagar College of Engineering Department of Information Science & Engineering Belagavi India Visvesveraya Technological University Dayananda Sagar College of Engineering Department of Computer Science & Engineering (Data Science) Belagavi India

ISBN: (数字)9798350375442

ISBN: (纸本)9798350375459

In country such as India due to monsoon and frequent climatic changes leads to natural disasters such as floods, drought, landslides, cyclones, forest fire, earthquake and so on. The post disaster badly impacts on human life, economy and environment. As remote sensing technology advanced, the satellite images with high resolution can be processed and analyzed to get the required results. These analyzed results can be used for rescue operations after disaster or to predict any disasters before their occurrences. Some of the applications such as post disaster event management widely use onboard processing to get faster analyzed results. Various operations such as satellite image- enhancement, compression, classification and segmentation are performed on satellite images. Results of these techniques mainly used in applications such as disaster management, Land Use Land Cover, Change detection, landslide and many more. In recent days, satellite image processing are performed using new technical aspects such as deep learning, Artificial intelligence and machine learning to get fast and more accurate results. Edge-Computing-Enabled Deep Learning and Leveraging Deep Learning are the approaches used for satellite image enhancement. Panoptic Segmentation of Multispectral Satellite Images approaches are mainly used for satellite image segmentation. Deep Learning based approach and Genererative Adverserial network architecture are applied to get change detection in satellite images. Lossless algorithm for hyperspectral data compression and neural network lossy compression techniques are preferably used for satellite image compression. Approaches such as Hybrid data model centric approach and Multiscale Convolutional neural network are applied to perform object detection. And also machine learning algorithms are mainly used for classification of satellite images. This paper provides comparative study survey on these various approaches used for processing of satellite images. ©

关键词： convolutional neural network deep learning disaster remote sensing satellite images

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Towards Optimal Communication Complexity in distributed Non-Convex Optimization 36

Towards Optimal Communication Complexity in Distributed Non-...

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36th conference on neural Information processing Systems (NeurIPS)

作者： Patel, Kumar Kshitij Wang, Lingxiao Woodworth, Blake Bullins, Brian Srebro, Nati TTIC Chicago IL 60637 USA INRIA SIERRA Paris France Purdue Univ W Lafayette IN 47907 USA

ISBN: (纸本)9781713871088

We study the problem of distributed stochastic non-convex optimization with intermittent communication. We consider the full participation setting where M machines work in parallel over R communication rounds and the partial participation setting where M machines are sampled independently every round from some meta-distribution over machines. We propose and analyze a new algorithm that improves existing methods by requiring fewer and lighter variance reduction operations. We also present lower bounds, showing our algorithm is either optimal or almost optimal in most settings. Numerical experiments demonstrate the superior performance of our algorithm.

关键词： Convex optimization

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Design and Application of Web-based Education Management System using Split and Kernel based Residual network

Design and Application of Web-based Education Management Sys...

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2024 International conference on Intelligent Algorithms for Computational Intelligence Systems, IACIS 2024

作者： Wang, Yan South West Minzu University School of Public Adminisration Chengdu China

ISBN: (纸本)9798350360660

A web based education management system is established to develop education system by enhancing quality of education and teaching model. However, the existing resource allocation model and teaching in web-based education system has limitations such as ineffective teaching due to inappropriate resource allocation affect the education system. To overcome this problem, a Split and Kernel based Residual network (SK-ResNet) is proposed to improve quality of education by allocating resources effectively and enhance teaching model in the web based education system. The split technique in ResNet model, divide the tasks and distributed across multiple servers or devices. By splitting and kernel optimizing the tasks, the load on any single system allows the model for faster content processing and delivery. At first, the data are acquired and fed into functional structure of education system, and finally analyzed and integrated into cloud computing based on SK-ResNet model. The experimental results of the proposed method achieved accuracy of 87.29% which is greater when compared to existing method such as Deep neural network (DNN) and Hybrid Deep Learning (HDL) model. © 2024 IEEE.

关键词： Cloud platforms

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Design of Real-time Object Recognition Algorithm Based on Deep Convolution neural network

Design of Real-time Object Recognition Algorithm Based on De...

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2023 International conference on Internet of Things, Robotics and distributed Computing, ICIRDC 2023

作者： Wang, Shuqin Zibo Technician College Shandong Zibo255000 China

ISBN: (纸本)9798350375275

In recent years, with the rapid development of computer vision and deep learning technology, real-time object recognition has become more and more important in various application fields. This research is devoted to designing a real-time object recognition algorithm based on Deep Convolution neural network (DCNN), aiming at overcoming the trade-off between accuracy and real-time performance of traditional methods. In this study, a DCNN is trained by using a large-scale labeled data set, so that it can learn rich feature representation. By introducing the hierarchical structure of deep network, the abstract ability of the model to objects is improved, and its generalization performance is enhanced by data enhancement technology. Through careful design of network structure and parameter adjustment, the model performs well in real-time object recognition tasks. In order to optimize real-time processing, we adopt a series of acceleration and optimization strategies to ensure that the algorithm can run quickly and reliably in various environments. Experiments show that the proposed algorithm achieves superior performance on several public data sets. Compared with traditional methods, the accuracy is improved by more than 8.26%, while maintaining high efficiency in real-time. The real-time object recognition algorithm based on DCNN proposed in this study has important potential value in practical application, which provides new technical support for promoting the development and application of intelligent systems. © 2023 IEEE.

关键词： Intelligent systems

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distributed Distributionally Robust Optimization with Non-Convex Objectives 36

Distributed Distributionally Robust Optimization with Non-Co...

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36th conference on neural Information processing Systems (NeurIPS)

作者： Jiao, Yang Yang, Kai Song, Dongjin Tongji Univ Shanghai Peoples R China Univ Connecticut Storrs CT USA

ISBN: (纸本)9781713871088

Distributionally Robust Optimization (DRO), which aims to find an optimal decision that minimizes the worst case cost over the ambiguity set of probability distribution, has been widely applied in diverse applications, e.g., network behavior analysis, risk management, etc. However, existing DRO techniques face three key challenges: 1) how to deal with the asynchronous updating in a distributed environment;2) how to leverage the prior distribution effectively;3) how to properly adjust the degree of robustness according to different scenarios. To this end, we propose an asynchronous distributed algorithm, named Asynchronous Single-looP alternatIve gRadient projEction (ASPIRE) algorithm with the itErative Active SEt method (EASE) to tackle the distributed distributionally robust optimization (DDRO) problem. Furthermore, a new uncertainty set, i.e., constrained D-norm uncertainty set, is developed to effectively leverage the prior distribution and flexibly control the degree of robustness. Finally, our theoretical analysis elucidates that the proposed algorithm is guaranteed to converge and the iteration complexity is also analyzed. Extensive empirical studies on real-world datasets demonstrate that the proposed method can not only achieve fast convergence, and remain robust against data heterogeneity as well as malicious attacks, but also tradeoff robustness with performance.

关键词： Risk management

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BioLCNet: Reward-Modulated Locally Connected Spiking neural networks 8th

BioLCNet: Reward-Modulated Locally Connected Spiking Neural ...

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8th Annual International conference on Machine Learning, Optimization and Data science (LOD)

作者： Ghaemi, Hafez Mirzaei, Erfan Nouri, Mahbod Kheradpisheh, Saeed Reza Politecn Torino Turin Italy Univ Tehran Tehran Iran Univ Edinburgh Edinburgh Midlothian Scotland Shahid Beheshti Univ Tehran Iran

ISBN: (纸本)9783031258909;9783031258916

Brain-inspired computation and information processing alongside compatibility with neuromorphic hardware have made spiking neural networks (SNN) a promising method for solving learning tasks in machine learning (ML). Spiking neurons are only one of the requirements for building a bio-plausible learning model. network architecture and learning rules are other important factors to consider when developing such artificial agents. In this work, inspired by the human visual pathway and the role of dopamine in learning, we propose a reward-modulated locally connected spiking neural network, BioLCNet, for visual learning tasks. To extract visual features from Poisson-distributed spike trains, we used local filters that are more analogous to the biological visual system compared to convolutional filters with weight sharing. In the decoding layer, we applied a spike population-based voting scheme to determine the decision of the network. We employed Spike-timing-dependent plasticity (STDP) for learning the visual features, and its reward-modulated variant (R-STDP) for training the decoder based on the reward or punishment feedback signal. For evaluation, we first assessed the robustness of our rewarding mechanism to varying target responses in a classical conditioning experiment. Afterwards, we evaluated the performance of our network on image classification tasks of MNIST and XOR MNIST datasets.

关键词： Spiking neural networks (SNN) Bio-plausible learning Spike-timing dependent plasticity (STDP) Image classification

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Residual network Based Bidirectional Gated Recurrent Unit for Speech Recognition Using Speech Signals 2

Residual Network Based Bidirectional Gated Recurrent Unit fo...

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2nd International conference on distributed Computing and Optimization Techniques, ICDCOT 2024

作者： Naga, Raju M. Jayachandran, T. Alzubaidi, Laith H. Mazumder, Debarshi Praveena, Hirald Dwaraka Gst Gitam Department of Cse Karnataka Nagadenahalli India Nandha Engineering College Department of Ece Tamilnadu Erode India The Islamic University Najaf Iraq Nitte Meenakshi Institute of Technology Department of Ai and Ds Bengaluru India School of Engineering Mohan Babu University Erstwhile Sree Vidya Nikethan Engineering College Department of Ece Andhra Pradesh India

ISBN: (纸本)9798350382952

Speech Recognition is challenging research ins speech processing field of research. Different method has available in previous research that attained high accuracy. However, accuracy of speech recognition is not increased because of irrelevant features in recognition stage. In this research, proposed a Residual network based Bidirectional Gated Recurrent Unit (ResNet based Bi-GRU) method for speech recognition using Libri Speech dataset. The signals from data are pre-processed and feature extraction is performed through Mel Frequency Cepstral Coefficients (MFCC) that extract relevant features. Then, the extracted features are fed to classification stage which recognized the speech signals with high accuracy and less error rates. Performance of developed method is evaluated to metrics of Word Error Rate (%), Character Error Rate (CER), accuracy and f1-score. Developed technique obtained less WER of 4.55%, CER of 4.1%, accuracy of 96.71% and f1-score of 96.15% that is effective than existing methods like Recurrent neural network (RNN) -Gated Recurrent Unit (GRU) and Long Short-Term Memory (LSTM). © 2024 IEEE.

关键词： Speech recognition

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GRADIENT INVERSION ATTACKS ON ACOUSTIC SIGNALS: REVEALING SECURITY RISKS IN AUDIO RECOGNITION SYSTEMS 49

GRADIENT INVERSION ATTACKS ON ACOUSTIC SIGNALS: REVEALING SE...

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2024 IEEE International conference on Acoustics, Speech, and Signal processing, ICASSP 2024

作者： Ovi, Pretom Roy Gangopadhyay, Aryya Center for Real-time Distributed Sensing and Autonomy University of Maryland Baltimore County United States

ISBN: (纸本)9798350344851

With a greater emphasis on data confidentiality and legislation, distributed training and collaborative machine learning algorithms are being developed to protect sensitive private data. Gradient exchange has become a widely used practice in those multi-node machine learning systems. But with the advent of gradient inversion attacks, it is already established that private training data can be revealed from the gradients. Gradient inversion attacks covertly spy on gradient updates and backtrack from the gradients to obtain information about the raw data. Although this attack has been widely studied in computer vision and natural language processing tasks, understanding the impact of this attack on acoustic signals still requires a comprehensive investigation. To the best of our knowledge, we are the first to explore gradient inversion attacks on acoustic signals by extracting the speakers' voices from an audio recognition system. Here, we design a new application of gradient inversion attack to retrieve the audio signal used for training the deep learning model, irrespective of whether the audio has undergone conversion into mel-spectrogram or MFCC representations prior to feed to neural network. Experimental results demonstrate the capability of our attack method to extract the input vectors of the audio data from the gradients, which highlight the security risks in revealing the sensitive audio data from highly secured systems. We also discuss several possible strategies as countermeasures and their effectiveness to prevent the attack. © 2024 IEEE.

关键词： Adversarial attacks Audio Data privacy

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