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Accelerate Graph neural network Training by Reusing Batch Data on GPUs

Accelerate Graph Neural Network Training by Reusing Batch Da...

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IEEE International conference on Performance, Computing and Communications (IPCCC)

作者： Zhejiang Ran Zhiquan Lai Lizhi Zhang Dongsheng Li National Key Laboratory of Parallel and Distributed Processing School of Computer National University of Defense Technology Changsha China

ISBN: (纸本)9781665443326

With the increasing adoption of graph neural networks (GNNs) in the graph-based deep learning community, various graph programming frameworks and models have been developed to improve the productivity of GNNs. The current GNN frameworks choose GPU as an essential tool to accelerate GNN training. However, it is still challenging to train GNNs on large graphs with limited GPU memory. Unlike traditional neural networks, generating mini-batch data by sampling in GNNs requires some complicated tasks such as traversing the graph to select neighboring nodes and gathering their features. This process takes up most of the training and we find the main bottleneck comes from transferring nodes features from CPU to GPU through limited bandwidth. In this paper, We propose a method Reusing Batch Data for the problem of data transmission. This method utilizes the similarity between adjacent mini-batches to reduce repeated data transmission from CPU to GPU. Furthermore, to reduce the overhead introduced by this method, we design a fast algorithm based on GPU to detect repeated nodes’ data and achieve shorter additional computation time. Evaluations on three representative GNN models show that our method can reduce transmission time by up to 60% and speed the end-to-end GNN training by up to 1.79× over the state-ofthe-art baselines. Besides, Reusing Batch Data can effectively save GPU memory footprint by about 19% to 40% while still reducing the training time compared to the static cache strategy.

关键词： Training Productivity Deep learning Design methodology conferences Graphics processing units Programming

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END-TO-END STREAMING KEYWORD SPOTTING 44

END-TO-END STREAMING KEYWORD SPOTTING

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44th IEEE International conference on Acoustics, Speech and Signal processing (ICASSP)

作者： Alvarez, Raziel Park, Hyun-Jin Google Inc Mountain View CA 94043 USA

ISBN: (纸本)9781479981311

We present a system for keyword spotting that, except for a front-end component for feature generation, it is entirely contained in a deep neural network (DNN) model trained "end-to-end" to predict the presence of the keyword in a stream of audio. The main contributions of this work are, first, an efficient memoized neural network topology that aims at making better use of the parameters and associated computations in the DNN by holding a memory of previous activations distributed over the depth of the DNN. The second contribution is a method to train the DNN, end-to-end, to produce the keyword spotting score. This system significantly outperforms previous approaches both in terms of quality of detection as well as size and computation.

关键词： deep neural networks keyword spotting audio processing embedded speech recognition

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9th EAI International conference on Cloud Computing, CloudComp 2019 and the 4th EAI International conference on Smart Grid and Innovative Frontiers in Telecommunications, SmartGIFT 2019

9th EAI International Conference on Cloud Computing, CloudCo...

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9th EAI International conference on Cloud Computing, CloudComp 2019 and the 4th EAI International conference on Smart Grid and Innovative Frontiers in Telecommunications, SmartGIFT 2019

ISBN: (纸本)9783030485122

The proceedings contain 55 papers. The special focus in this conference is on Cloud Computing. The topics include: Rendering of Three-Dimensional Cloud Based on Cloud Computing;distributed Stochastic Alternating Direction Method of Multipliers for Big Data Classification;personalized Recommendation Algorithm Considering Time Sensitivity;cloud-Based Master Data Platform for Smart Manufacturing Process;a Semi-supervised Classification Method for Hyperspectral Images by Triple Classifiers with Data Editing and Deep Learning;A Survey of Image Super Resolution Based on CNN;design and Development of an Intelligent Semantic Recommendation System for Websites;a Lightweight neural network Combining Dilated Convolution and Depthwise Separable Convolution;resource Allocation Algorithms of Vehicle networks with Stackelberg Game;research on Coordination Control Theory of Greenhouse Cluster Based on Cloud Computing;a Multi-objective Computation Offloading Method in Multi-cloudlet Environment;anomalous Taxi Route Detection System Based on Cloud Services;collaborative Recommendation Method Based on Knowledge Graph for Cloud Services;efficient Multi-user Computation Scheduling Strategy Based on Clustering for Mobile-Edge Computing;Grazing Trajectory Statistics and Visualization Platform Based on Cloud GIS;Cloud-Based AGV Control System;a Parallel Drone Image Mosaic Method Based on Apache Spark;cycleSafe: Safe Route Planning for Urban Cyclists;prediction of Future Appearances via Convolutional Recurrent neural networks Based on Image Time Series in Cloud Computing;video Knowledge Discovery Based on Convolutional neural network;time-Varying Water Quality Analysis with Semantical Mining Technology;a Survey of QoS Optimization and Energy Saving in Cloud, Edge and IoT;data-Driven Fast Real-Time Flood Forecasting Model for processing Concept Drift;intelligent System Security Event Description Method.

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A mechanistic multi-area recurrent network model of decision-making 21

A mechanistic multi-area recurrent network model of decision...

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Proceedings of the 35th International conference on neural Information processing Systems

作者： Michael Kleinman Chandramouli Chandrasekaran Jonathan C. Kao University of California Los Angeles Boston University

ISBN: (纸本)9781713845393

Recurrent neural networks (RNNs) trained on neuroscience-based tasks have been widely used as models for cortical areas performing analogous tasks. However, very few tasks involve a single cortical area, and instead require the coordination of multiple brain areas. Despite the importance of multi-area computation, there is a limited understanding of the principles underlying such computation. We propose to use multi-area RNNs with neuroscience-inspired architecture constraints to derive key features of multi-area computation. In particular, we show that incorporating multiple areas and Dale's Law is critical for biasing the networks to learn biologically plausible solutions. Additionally, we leverage the full observability of the RNNs to show that output-relevant information is preferentially propagated between areas. These results suggest that cortex uses modular computation to generate minimal sufficient representations of task information. More broadly, our results suggest that constrained multi-area RNNs can produce experimentally testable hypotheses for computations that occur within and across multiple brain areas, enabling new insights into distributed computation in neural systems.

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Optimized WLAN Channel Allocation based on Gibbs Sampling with Busy Prediction using a Probabilistic neural network 3

Optimized WLAN Channel Allocation based on Gibbs Sampling wi...

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3rd International conference on Communications, Signal processing, and their Applications (ICCSPA)

作者： Webber, Julian Mehbodniya, Abolfazl Yano, Kazuto Suzuki, Yoshinori Adv Telecommun Res Inst Int ATR Kyoto 6190288 Japan Osaka Univ Toyonaka Osaka 5608531 Japan Kuwait Coll Sci & Technol Kuwait Kuwait

ISBN: (纸本)9781728120850

This paper studies a channel allocation algorithm for next generation wireless local area networks ( WLANs). The aim is to maintain a fairness in usage of all Wi-Fi channels in a network. A probabilistic neural network ( PNN) is employed for predicting the upcoming busy-line status on each channel and this information is distributed to other access-points ( APs) in the network. The probability of switching to a new less-busy channel which is modeled by a Markov chain is solved by sampling from a Gibbs distribution with low computational complexity. Simulation results show that the application layer and MAC layer transmission delays are reduced by up to 10% and 5% respectively with respect to the current IEEE 802.11ac system.

关键词： WLAN channel allocation IEEE802.11 probabilistic neural network Gibbs sampler

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Evaluating Built-in ECC of FPGA on-chip Memories for the Mitigation of Undervolting Faults 27

Evaluating Built-in ECC of FPGA on-chip Memories for the Mit...

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27th Euromicro International conference on Parallel, distributed and network-Based processing (PDP)

作者： Salami, Behzad Unsal, Osman S. Cristal Kestelman, Adrian BSC Barcelona Spain Univ Politecn Cataluna Barcelona Spain CSIC Spanish Natl Res Council IIIA Artificial Intelligence Res Inst Madrid Spain

ISBN: (纸本)9781728116440

Voltage underscaling below the nominal level is an effective solution for improving energy efficiency in digital circuits, e.g., Field Programmable Gate Arrays (FPGAs). However, further undervolting below a safe voltage level and without accompanying frequency scaling leads to timing related faults, potentially undermining the energy savings. Through experimental voltage underscaling studies on commercial FPGAs, we observed that the rate of these faults exponentially increases for on-chip memories, or Block RAMs (BRAMs). To mitigate these faults, we evaluated the efficiency of the built-in Error-Correction Code (ECC) and observed that more than 90% of the faults are correctable and further 7% are detectable (but not correctable). This efficiency is the result of the single-bit type of these faults, which are then effectively covered by the Single-Error Correction and Double-Error Detection (SECDED) design of the built-in ECC. Finally, motivated by the above experimental observations, we evaluated an FPGA-based neural network (NN) accelerator under low-voltage operations, while built-in ECC is leveraged to mitigate undervolting faults and thus, prevent NN significant accuracy loss. In consequence, we achieve 40% of the BRAM power saving through undervolting below the minimum safe voltage level, with a negligible NN accuracy loss, thanks to the substantial fault coverage by the built-in ECC.

关键词： Field programmable gate arrays Error correction codes Circuit faults Random access memory Power demand Artificial neural networks Computer crashes

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Cyberbullying detection based on word curve representations using B-spline interpolation 20

Cyberbullying detection based on word curve representations ...

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Proceedings of the 4th International conference on Future networks and distributed Systems

作者： Huu Tai Phung Huynh Khanh Ly Dang Minh Tuan Pham University of Science and Technology - The University of Danang Vietnam

ISBN: (纸本)9781450388863

With the advancement of technology and the rise of social media, people start to get worried about the increase of cyberbullying. Therefore, researches on cyberbullying have soared remarkably in recent years. Machine learning and natural language processing techniques are helpful to generate a model that automatically detects cyberbullying actions. However, most of the existing approaches do not preserve the information of word order. This problem causes a large loss in the text's meaning. In this study, we propose a new text representation based on the idea of making fix-length text data using B-Spline interpolation. Then, 1D-Convolutional neural network is used to classify cyberbullying and non-cyberbullying documents. We experiment the proposed method on a text dataset crawled from the site ***. The experimental result shows that the proposed methods outperform the conventional methods in cyberbullying detection

关键词： 1D-Convolutional neural networks

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8th International conference on Sciences of Electronics, Technologies of Information and Telecommunication, SETIT 2018

8th International Conference on Sciences of Electronics, Tec...

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8th International conference on Sciences of Electronics, Technologies of Information and Telecommunication, SETIT 2018

ISBN: (纸本)9783030210045

The proceedings contain 47 papers. The special focus in this conference is on Sciences of Electronics, Technologies of Information and Telecommunication. The topics include: A New Biomedical Text Summarization Method Based on Sentence Clustering and Frequent Itemsets Mining;An Implementation of InfluxDB for Monitoring and Analytics in distributed IoT Environments;Publish a Jason Agent BDI Capacity as Web Service REST and SOAP;FPGA Implementation of a Quantum Cryptography Algorithm;health Recommender Systems: A Survey;distributed Architecture of an Intrusion Detection System Based on Cloud Computing and Big Data Techniques;choosing the Right Storage Solution for the Corpus Management System (Analytical Overview and Experiments);requirements Imprecision of Data Warehouse Design Fuzzy Ontology-Based Approach - Fuzzy Connector Case;quantitative Prediction of Toxicity of Substituted Phenols Using Deep Learning;an Affective Tutoring System for Massive Open Online Courses;rationality Measurement for Jadex-Based Applications;a Continuous Optimization Scheme Based on an Enhanced Differential Evolution and a Trust Region Method;strided Convolution Instead of Max Pooling for Memory Efficiency of Convolutional neural networks;ear Recognition Based on Improved Features Representations;some Topological Indices of Polar Grid Graph;deep Elman neural network for Greenhouse Modeling;High Efficiency Multiplierless DCT Architectures;signature of Electronic Documents Based on the Recognition of Minutiae Fingerprints;person Re-Identification Using Pose-Driven Body Parts;automatic processing of Oral Arabic Complex Disfluencies;high Securing Cryptography System for Digital Image Transmission;A Novel DWTTH Approach for Denoising X-Ray Images Acquired Using Flat Detector;recent Advances in Fire Detection and Monitoring Systems: A Review;preface.

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Two-Stream Convolutional neural network for Action Recognition in Radar 6th

Two-Stream Convolutional Neural Network for Action Recogniti...

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6th conference on Signal and Information processing, networking and Computers, ICSINC 2019

作者： Zhou, Hao He, Yuan Huang, Hai Jing, Xiaojun Laboratory of Biological Recognition and Information Application Beijing University of Posts and Telecommunications Beijing China Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education Beijing University of Posts and Telecommunications Beijing100876 China

ISBN: (纸本)9789811541629

In the domain of human action classification, information obtained by optical equipment is used as the input source in most case. However, these optical devices are often affected by environmental factors, which bring some trouble to human motion recognition. The radar signal can avoid these situations, therefore the radar signal has been more and more significant part in this domain. In recent research, most researchers used the Micro-Doppler spectrogram of radar to classify the action. It only makes use of the time-frequency domain information, but ignores the spatial domain information. However, for the radar signal of the human motion, there should be some differences between the information in the time-frequency domain and the information in the other domain such as the space domain. In this case, we employed a CNN with two streams, which can use both Micro-Doppler spectrogram and the echo of the radar to recognize the action. In our architecture, there are two separate CNN processing streams for extracting the features of two stream of input, and the features are combined with a late network. The experiment proves that our method has better accuracy than the input of a single stream. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Convolution

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Neuro-Correlates of the eBICA Model 10th

Neuro-Correlates of the eBICA Model

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10th Annual International conference on Biologically Inspired Cognitive Architectures, BICA 2019

作者： Ushakov, Vadim L. Orlov, Vyacheslav A. Kartashov, Sergey I. Shigeev, Sergey V. Samsonovich, Alexei V. Moscow Russia National Research Center "Kurchatov Institute" Moscow Russia Bureau of Forensic Medical Expertise Moscow Health Care Department Moscow Russia

ISBN: (纸本)9783030257187

The article presents a new approach to building semantic maps based on the use of the eBICA model and the calculation of neurophysiological correlates of behavioral motives based on fMRI data. In this study, a social videogame paradigm was used in combination with fMRI and other recording tools. The obtained data show the complex neural network dynamics of behavioral motives distributed on the cortical and subcortical regions of the brain, related to the interaction of a person with the collaborative non-player character powered by the eBICA cognitive architecture. © 2020, Springer Nature Switzerland AG.

关键词： network architecture

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