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A Bit Level Acceleration of Mixed Precision neural network

A Bit Level Acceleration of Mixed Precision Neural Network

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International conference on Parallel and distributed Systems (ICPADS)

作者： Dehui Qiu Jing Wang Yi Hou Weigong Zhang Lan Gao Information Engineering College Capital Normal University Beijing China School of Information Renmin University of China Beijing China

With the growth of the convolutional neural network (CNN) parameters, the hardware resources become limited when deploying CNN models. Single bit-width quantization may lead to degradation of accuracy, while mixed-precision quantization models maintain higher accuracy. However, current mixed-precision quantization accelerators only consider the algorithm level quantization and fixed-bit-width processing elements (PEs) without fully utilizing the resources. Therefore, we propose a neural network acceleration architecture based on bit-level computational units to improve resource utilization and throughput of mixed-precision quantization accelerators. The 2-bit and 3-bit low-bit computational units are designed to implement the high-bit quantization. We also propose spatio-temporal fusion to satisfy the unique bit widths in each layer with mixed precision quantization. In particular, we use the 2-bit and 3-bit computational units to achieve dynamic layer-level quantization. We also discuss various combinations of different bit widths, which can be dynamically implemented according to the requirements of accuracy, execution time, etc. The proposed acceleration architecture is implemented in Verilog, verified using three network models: ResNet18, VGG7 and LeNet-5, and tested against three accelerators: Eyeriss, Stripes and Bit Fusion. The experimental results show that our accelerators provide more accuracy and the grouping operation reduces the area overhead. It provides a 3.08 to 3.54 times acceleration ratio and 2.4 to 2.57 times reduction in energy consumption on the three networks.

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Proceedings - 2021 IEEE 27th International conference on Parallel and distributed Systems, ICPADS 2021

Proceedings - 2021 IEEE 27th International Conference on Par...

引用

27th IEEE International conference on Parallel and distributed Systems, ICPADS 2021

ISBN: (纸本)9781665408783

The proceedings contain 117 papers. The topics discussed include: a novel iBeacon deployment scheme for indoor pedestrian positioning;near-optimal resource allocation and virtual network function placement at network edges;predicting downside in stock market using knowledge and news data;two-layer traffic signal optimization: a EDGE-assisted pressure balance approach based on cooperative game;passenger payment willingness prediction by static and dynamic multi-dimensional ticket attributes fusion;towards network-accelerated ML-based distributed computer vision systems;ATO-EDGE: adaptive task offloading for deep learning in resource-constrained edge computing systems;anti-replay: a fast and lightweight voice replay attack detection system;an electromagnetic covert channel based on neural network architecture;choosing appropriate AI-enabled edge devices, not the costly ones;and BRNN-GAN: generative adversarial networks with bi-directional recurrent neural networks for multivariate time series imputation.

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Brain Tumour Segmentation and Volume Estimation using Efficient Convolution neural network for MRI Images

Brain Tumour Segmentation and Volume Estimation using Effici...

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distributed Computing and Electrical Circuits and Electronics (ICDCECE), IEEE International conference on

作者： R Jaichandran R Shobana S Senthilkumar A Srinivasan Renuka Arora Ashok Kumar Department of Computer Science and Engineering Aarupadai Veedu Institute of Technology Organization Vinayaka Mission’s Research Foundation (Deemed to be University) Paiyanoor India Department of Computer Science and Engineering Jagannath University NCR Haryana Department of Computer Science and Engineering Banasthali Vidyapith Rajasthan India

A brain tumor is a type of blood clot in the form of cerebral disease. A brain tumor is a way to view and Magnetic Resonance Imaging (MRI) image detail is required. It is difficult to distinguish between normal tissue and tumor tissue from the brain. To should be an accurate analysis of brain tumours. Brain tumour segmentation is one of the most important and difficult tasks in the medical imaging field. Manual classification with human assistance can lead to incorrect predictions and diagnoses. Moreover, it is a difficult task when there is a large amount of data to help. Brain tumours are highly heterogeneous in appearance, and similarities between tumor and normal tissue make it difficult to extract tumour regions from images. In this paper, we proposed a method for extracting brain tumours from 2D magnetic resonance brain images (MRI) by a Fuzzy C-Means clustering algorithm with a traditional classifier and a convolutional neural network. Initially the pre-processing was carried out to apply the filter for image normalization with wiener filter and Robust active shape model was applied to predict the voluminous of data presence and segmentation was carried out through fuzzy c-means clustering and selected features was trained into optimize fuzzy c-means convolution neural network (FCM-CNN). This proposed system produces high detection accuracy in precision rate as well compared to other system.

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Deep neural network aided Sparse Bayesian Learning for Wireless Access Channel Estimation in mm-Wave Massive MIMO Cloud Radio Access network Systems 1

Deep Neural Network aided Sparse Bayesian Learning for Wirel...

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1st International conference on Futuristic Technologies, INCOFT 2022

作者： Datta, Jayanta Zabala-Blanco, David Soria, Francisco Ruben Castillo University of Chile Department of Electrical Engineering Santiago Chile Universidad Católica de Maule Department of Computer Science and Industry Talca Chile Autonomous University of San Luis Potosi Telecommmunications Engineering Faculty of Sciences San Luis Potosi Mexico

ISBN: (纸本)9781665450461

Cloud Radio Access network systems with mmWave Massive MIMO framework can be considered as a potential candidate for next generation wireless communications due to its promise of increased spectral efficiency and distributed signal processing capability. State-of-the-art compressive sensing algorithms like sparse Bayesian learning can exploit the inherent sparsity of the mm-Wave wireless channels to estimate the channel connecting the remote radio head and the user equipment in the wireless access link. The performance of the sparse Bayesian learning based channel estimation can be adversely affected by impairments due to optical fiber based front-haul channel and quantization noise. As a result, it is necessary to compensate for the performance degradation by applying methods which can combat the effects of the front-haul channel. Contemporary research has demonstrated the capability of deep learning algorithms in signal enhancement under low signal-to-noise ratio conditions, such as hybrid beamforming design, channel estimation as well as feedback of channel state in heterogeneous multi-antenna wireless systems. Motivated by their de-noising and signal prediction capabilities, convolutional long short term memory networks are employed in this work to jointly remove the quantization noise and optical fiber impairment due to the front-haul channel, which can improve the performance of sparse Bayesian learning in estimating the wireless access channel at the base-band processing unit. Computer simulation results show that the proposed methodology performs well under low signal-to-noise ratio conditions. © 2022 IEEE.

关键词： Signal to noise ratio

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An Edge Attribute-Wise Partitioning and distributed processing of R-GCN Using GPUs

An Edge Attribute-Wise Partitioning and Distributed Processi...

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26th International conference on Parallel and distributed Computing (Euro-Par)

作者： Kibata, Tokio Tsukada, Mineto Matsutani, Hiroki Keio Univ 3-14-1 Hiyoshi Yokohama Kanagawa 2238522 Japan

ISBN: (纸本)9783030715939;9783030715922

GCN (Relational Graph Convolutional network) is one of GNNs (Graph neural networks). The model tries predicting latent information by considering directions and types of edges in graph-structured data, such as knowledge bases. The model builds weight matrices to each edge attribute. Thus, the size of the neural network increases linearly with the number of edge types. Although GPUs can be used for accelerating the R-GCN processing, there is a possibility that the size of weight matrices exceeds GPU device memory. To address this issue, in this paper, an edge attribute-wise partitioning is proposed for R-GCN. The proposed partitioning divides the model and graph data so that R-GCN can be accelerated by using multiple GPUs. Also, the proposed approach can be applied to sequential execution on a single GPU. Both the cases can accelerate the R-GCN processing with large graph data, where the original model cannot be fit into a device memory of a single GPU without partitioning. Experimental results demonstrate that our partitioning method accelerates R-GCN by up to 3.28 times using four GPUs compared to CPU execution for a dataset with more than 1.6 million nodes and 5 million edges. Also, the proposed approach can accelerate the execution even with a single GPU by 1.55 times compared to the CPU execution for a dataset with 0.8 million nodes and 2 million edges.

关键词： GPU R-GCN GNN Graph data Knowledge base

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network Traffic Classification Using distributed ML-Based Data Parallelization Approach

Network Traffic Classification Using Distributed ML-Based Da...

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Canadian conference on Electrical and Computer Engineering (CCECE)

作者： Muhammad Musa K Abid Hussain Muhammad Jaseemuddin Rasha Kashef Department of ECE Toronto Metropolitan University Toronto Canada

The large datasets related to network traffic flow classification in the internet benefit machine learning (ML) and deep learning (DL) models for more accurate classification, which is used in many applications such as in detecting traffic anomaly for prevention of potential cyber-attacks. Data Parallelization allows for faster training times on large datasets as shown in our results, and it is also beneficial in the cloud-edge environment by allowing efficient distribution of computation and data across multiple nodes. We deployed Convolutional neural network (CNN), Long Short-Term Memory (LSTM), advanced hybrid Convolutional LSTM (ConvLSTM), Convolutional GRU (ConvGRU), and XGBoost algorithm using Data Parallelization approach. The experimental setup was implemented in the cloud and parallel training was executed using Nvidia Tesla Graphics processing Units (GPUs). Lastly comparison of the performance metric results is presented between the non-parallel centralized (single node) and data parallel distributed approaches.

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21st International conference on Computer Information Systems and Industrial Management Applications, CISIM 2022

21st International Conference on Computer Information System...

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21st International conference on Computer Information Systems and Industrial Management Applications, CISIM 2022

ISBN: (纸本)9783031105388

The proceedings contain 28 papers. The special focus in this conference is on Computer Information Systems and Industrial Management Applications. The topics include: Augmentation of Accelerometer and Gyroscope Signals in Biometric Gait Systems;Evolutionary Optimization of UAVs Deployment for k-Coverage Problem;Embedded Processor Design in FPGA by ASMD-FSMD and FSM-Single Techniques;an Efficient Data Distribution Strategy for distributed Graph processing System;mobile Phone as a 6DoF Motion Controller;application of Graph Document Model for Classification of Agricultural Scientific Papers in Polish;integrated Models-Driven Framework to Generate Various Online and Print Tests;hybrid Model of Tourism Recommendation Software Development;Application of Continuous Embedding of Viral Genome Sequences and Machine Learning in the Prediction of SARS-CoV-2 Variants;Food Classification from Images Using a neural network Based Approach with NVIDIA Volta and Pascal GPUs;bird Identification from the Thamnophilidae Family at the Andean Region of Colombia;predictive Model of Cardiovascular Diseases Implementing Artificial neural networks;user Interface-Based in Machine Learning as Tool in the Analysis of Control Loops Performance and Robustness;analysis of Pre-trained Convolutional neural network Models in Diabetic Retinopathy Detection Through Retinal Fundus Images;knowledge Management: Effects on Innovation in Micro, Small, and Medium-Sized Export Enterprises;international Purchase Transactions: An Analysis of the Decision Cycles in Colombian Companies’ Operations;drivers of Eco-innovation in Industrial Clusters - A Case Study in the Colombian Metalworking Sector;design of a Wearable Assistive System for Visually Impaired People;blood Pressure Estimation from Photoplethysmography Signals by Applying Deep Learning Techniques;a Recommender System for Digital Newspaper Readers Based on Random Forest;preface;a New Approach for Image Thinning.

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Unsupervised network Intrusion Detection Using Convolutional neural networks

Unsupervised Network Intrusion Detection Using Convolutional...

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IEEE Annual Computing and Communication Workshop and conference (CCWC)

作者： Shumon Alam Yasin Alam Suxia Cui Cajetan M. Akujuobi College of Engineering Prairie View A&M University Prairie View USA Computer Science Dept. Texas A&M University College Station USA Electrical & Comp. Eng. Dept. Prairie View A&M University Prairie View USA

Unsupervised Machine Learning (ML) is more desirable than supervised ML-based network intrusion detection techniques. Convolutional neural network (CNN) performs excellently in tasks related to image processing and computer vision applications as a supervised learning (SL) model, but SL is not suitable for a zero-day attack detection for network intrusion detection (IDS) system. In this work, the power of CNN in conjunction with autoencoder (AE) is used to develop unsupervised machine learning techniques to detect anomalies in network traffic. Two models are developed: CNN-based pseudo-AE and CNN-based classical AE models. The PVAMU-DDoS2020 dataset is used for training and testing the models. The results show the models are efficient in detecting anomaly (distributed denial-of-service) traffic for the unseen traffic flows from the PVAMU-DDoS2020 in an unsupervised fashion.

关键词： Training network intrusion detection Computer architecture Telecommunication traffic Machine learning Convolutional neural networks Task analysis

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ATTENTION BASED network FOR NO-REFERENCE UGC VIDEO QUALITY ASSESSMENT

ATTENTION BASED NETWORK FOR NO-REFERENCE UGC VIDEO QUALITY A...

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IEEE International conference on Image processing (ICIP)

作者： Yi, Fuwang Chen, Mianyi Sun, Wei Min, Xiongkuo Tian, Yuan Zhai, Guangtao Shanghai Jiao Tong Univ Inst Image Commun & Network Engn Shanghai Peoples R China Tencent Social Commun Lab Shenzhen Peoples R China

ISBN: (纸本)9781665441155

The quality assessment of user-generated content (UGC) videos is a challenging problem due to the absence of reference videos and their complex distortions. Traditional no-reference video quality assessment (NR-VQA) algorithms mainly target specific synthetic distortions. Less attention has been paid to authentic distortions in UGC videos, which are not distributed evenly in both the spatial and temporal domains. In this paper, we propose an end-to-end neural network model for UGC video quality assessment based on the attention mechanism. The key step in our approach is to embed the attention modules in the feature extraction network, which effectively extracts local distortion information. In addition, to exploit the temporal perception mechanism of the human visual system (HVS), the gated recurrent unit (GRU) and temporal pooling layer are integrated into the proposed model. We validate the proposed model on three public in-the-wild VQA databases: KoNViD-1k, CVD2014, and LIVE-Qualcomm. Experimental results demonstrate that the proposed method outperforms state-of-the-art NR-VQA models. The implementation of our method is released at https://***/qingshangithub/AB-VQA.

关键词： No-reference video quality assessment user-generated content videos attention mechanism

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VARIANCE-CONSTRAINED LEARNING FOR STOCHASTIC GRAPH neural networkS

VARIANCE-CONSTRAINED LEARNING FOR STOCHASTIC GRAPH NEURAL NE...

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

作者： Gao, Zhan Isufi, Elvin Ribeiro, Alejandro Univ Penn Dept Elect & Syst Engn Philadelphia PA 19104 USA Delft Univ Technol Dept Intelligent Syst Delft Netherlands

ISBN: (纸本)9781728176055

Stochastic graph neural networks (SGNNs) are information processing architectures that can learn representations from data over random graphs. SGNNs are trained with respect to the expected performance, but this training comes with no guarantee about the deviation of particular output realizations around the optimal mean. To overcome this issue, we propose a learning strategy for SGNNs based on a variance constrained optimization problem, balancing the expected performance and the stochastic deviation. To handle the variance constraint in the stochastic optimization problem, training is undertaken in the dual domain. We propose an alternating primal-dual learning algorithm that updates the primal variable (SGNN parameters) with gradient descent and the dual variable with gradient ascent. We show the stochastic deviation is explicitly controlled through Chebyshev inequality and analyze the optimality loss induced by the primal-dual learning. Through numerical simulations, we observe a strong performance in expectation with a controllable deviation corroborating the theoretical findings.

关键词： Stochastic graph neural networks distributed learning variance constraint primal-dual learning

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