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A Survey on Graph Processing Accelerators:Challenges and Opportunities

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Journal of Computer Science & Technology 2019年第2期34卷 339-371页

作者： Chuang-Yi Gui Long Zheng Bingsheng He Cheng Liu Xin-Yu Chen Xiao-Fei Liao Hai Jin National Engineering Research Center for Big Data Technology and System School of Computer Science and Technology Huazhong University of Science and Technology Wuhan 430074 China Services Computing Technology and System Laboratory School of Computer Science and Technology Huazhong University of Science and Technology Wuhan 430074 China Cluster and Grid Computing Laboratory School of Computer Science and Technology Huazhong University of Science and Technology Wuhan 430074 China School of Computing National University of Singapore Singapore 117418 Singapore Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China

Graph is a well known data structure to represent the associated relationships in a variety of applications,e.g.,data science and machine *** a wealth of existing efforts on developing graph processing systems for improving the performance and/or energy efficiency on traditional architectures,dedicated hardware solutions,also referred to as graph processing accelerators,are essential and emerging to provide the benefits significantly beyond what those pure software solutions can *** this paper,we conduct a systematical survey regarding the design and implementation of graph processing ***,we review the relevant techniques in three core components toward a graph processing accelerator:preprocessing,parallel graph computation,and runtime *** also examine the benchmarks and results in existing studies for evaluating a graph processing ***,we find that there is not an absolute winner for all three aspects in graph acceleration due to the diverse characteristics of graph processing and the complexity of hardware *** finally present and discuss several challenges in details,and further explore the opportunities for the future research.

关键词： graph processing accelerator domain-specific architecture performance energy efficiency

Continual learning via inter-task synaptic mapping

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arXiv 2021年

作者： Fubing, Mao Weiwei, Weng Pratama, Mahardhika Kien Yee, Edward Yapp National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab. Cluster and Grid Computing Lab. School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China School of Computer Science and Engineering Nanyang Technological University Singapore Singapore Singapore Institute of Manufacturing Technology A*Star Singapore Singapore

Learning from streaming tasks leads a model to catastrophically erase unique experiences it absorbs from previous episodes. While regularization techniques such as LWF, SI, EWC have proven themselves as an effective avenue to overcome this issue by constraining important parameters of old tasks from changing when accepting new concepts, these approaches do not exploit common information of each task which can be shared to existing neurons. As a result, they do not scale well to large-scale problems since the parameter importance variables quickly explode. An Inter-Task Synaptic Mapping (ISYANA) is proposed here to underpin knowledge retention for continual learning. ISYANA combines task-to-neuron relationship as well as concept-to-concept relationship such that it prevents a neuron to embrace distinct concepts while merely accepting relevant concept. Numerical study in the benchmark continual learning problems has been carried out followed by comparison against prominent continual learning algorithms. ISYANA exhibits competitive performance compared to state of the arts. Codes of ISYANA is made available in https://***/ContinualAL/ISYANAKBS. © 2021, CC BY-NC-SA.

关键词： Neurons

可持续发展情景下丝绸之路沿线国家土地利用及其对陆地碳库影响

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Science Bulletin 2023年第7期68卷 740-749页

作者：曹敏田雅吴凯陈旻陈玉胡雪孙中昶左丽君林键骆磊朱瑞许振赐 Temenoujka Bandrova Milan Konecny 袁文平郭华东林珲闾国年 Key Laboratory of Virtual Geographic Environment Ministry of EducationNanjing Normal UniversityNanjing 210023China International Research Center of Big Data for Sustainable Development Goals Beijing 100094China Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application Nanjing 210023China School of Geography and Environment Jiangxi Normal UniversityNanchang 330022China Aerospace Information Research Institute Chinese Academy of SciencesBeijing 100094China The Key Laboratory of Urban Land Resources Monitoring and Simulation Ministry of Natural ResourcesShenzhen 518034China Sierra Nevada Research Institute University of CaliforniaMerced CA 95348USA Institute of High Performance Computing Agency for ScienceTechnology and ResearchSingapore 138632Singapore Department of Geography the University of Hong KongHong Kong 999077China Laboratory on Cartography University of ArchitectureCivil Engineering and GeodesySofia 1164Bulgaria Laboratory on Geoinformatics and Cartography Institute of GeographyMasaryk UniversityBrno 60177Czech Republic School of Atmospheric Sciences Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai)Sun Yat-sen UniversityZhuhai 519082China Key Laboratory of Poyang Lake Wetland and Watershed Research Ministry of EducationJiangxi Normal UniversityNanchang 330022China

可持续发展目标(SDGs)呼吁全世界共同采取行动,促进繁荣并保护地球.本研究面向SDGs构建了环境、粮食和经济等可持续发展情景,预测2020~2030年丝绸之路沿线国家的土地利用变化,并分析其对陆地碳库的影响。研究表明:未来土地利用变化及其... 详细信息

可持续发展目标(SDGs)呼吁全世界共同采取行动,促进繁荣并保护地球.本研究面向SDGs构建了环境、粮食和经济等可持续发展情景,预测2020~2030年丝绸之路沿线国家的土地利用变化,并分析其对陆地碳库的影响。研究表明:未来土地利用变化及其对陆地碳库的影响在不同情景下存在显著差异。在可持续环境情景下,林地得到了有效保护,相较于2020年中国林地碳储量增加了约0.60%;在可持续粮食情景下,耕地流失得到了缓解,尤其在南亚及东南亚地区的耕地面积呈现上升趋势:在可持续经济情景下,城市扩张速度加快,引起的碳损失量最多这项研究有助于推进丝绸之路沿线甚至全球可持续发展进程,为探寻可持续发展路径提供了科学依据。

关键词： Sustainable development goals(SDGs) Land-use projection Carbon pool Scenarios System dynamics model Cellular automata

Strong coupling of antiferromagnetic resonance with sub-THz cavity fields

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arXiv 2020年

作者： Bialek, Marcin Zhang, J. Yu, H. Ansermet, J.-Ph. Lausanne1015 Switzerland Fert Beijing Institute School of Microelectronics Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China

Strong coupling of electromagnetic cavity fields with antiferromagnetic spin waves in hematite (α-Fe2O3) was achieved above room temperature. A cube of hematite was placed in a metallic tube and transmission was measured, using a continuous-wave THz spectrometer. Spectra, collected as a function of temperature, reveal the formation of magnetic polaritons. © 2020, CC BY-SA.

关键词： Hematite

Attention-based Residual Speech Portrait Model for Speech to Face Generation

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arXiv 2020年

作者： Wang, Jianrong Hu, Xiaosheng Liu, Li Liu, Wei Yu, Mei Xu, Tianyi College of Intelligence and Computing Tianjin University Tianjin300350 China Shenzhen Research Institute of Big Data Chinese University of Hong Kong Shenzhen Guangdong Hong Kong

Given a speakers speech, it is interesting to see if it is possible to generate this speakers face. One main challenge in this task is to alleviate the natural mismatch between face and speech. To this end, in this paper, we propose a novel Attention-based Residual Speech Portrait Model (AR-SPM) by introducing the ideal of the residual into a hybrid encoder-decoder architecture, where face prior features are merged with the output of speech encoder to form the final face feature. In particular, we innovatively establish a tri-item loss function, which is a weighted linear combination of the L2-norm, L1-norm and negative cosine loss, to train our model by comparing the final face feature and true face feature. Evaluation on AVSpeech dataset shows that our proposed model accelerates the convergence of training, outperforms the state-of-the-art in terms of quality of the generated face, and achieves superior recognition accuracy of gender and age compared with the ground truth. Copyright © 2020, The Authors. All rights reserved.

关键词： Speech

Backdoor attack against speaker verification

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arXiv 2020年

作者： Zhai, Tongqing Li, Yiming Zhang, Ziqi Wu, Baoyuan Jiang, Yong Xia, Shu-Tao Tsinghua Shenzhen International Graduate School Tsinghua University Shenzhen China School of Data Science The Chinese University of Hong Kong Shenzhen China Secure Computing Lab of Big Data Shenzhen Research Institute of Big Data Shenzhen China PCL Research Center of Networks and Communications Peng Cheng Laboratory Shenzhen China

Speaker verification has been widely and successfully adopted in many mission-critical areas for user identification. The training of speaker verification requires a large amount of data, therefore users usually need to adopt third-party data (e.g., data from the Internet or third-party data company). This raises the question of whether adopting untrusted third-party data can pose a security threat. In this paper, we demonstrate that it is possible to inject the hidden backdoor for infecting speaker verification models by poisoning the training data. Specifically, we design a clustering-based attack scheme where poisoned samples from different clusters will contain different triggers (i.e., pre-defined utterances), based on our understanding of verification tasks. The infected models behave normally on benign samples, while attacker-specified unenrolled triggers will successfully pass the verification even if the attacker has no information about the enrolled speaker. We also demonstrate that existing backdoor attacks cannot be directly adopted in attacking speaker verification. Our approach not only provides a new perspective for designing novel attacks, but also serves as a strong baseline for improving the robustness of verification methods. The code for reproducing main results is available at https://***/zhaitongqing233/Back door-attack-against-speaker-verification. Copyright © 2020, The Authors. All rights reserved.

关键词： Speech recognition

Mesh Variational Autoencoders with Edge Contraction Pooling

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Mesh Variational Autoencoders with Edge Contraction Pooling

IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

作者： Yu-Jie Yuan Yu-Kun Lai Jie Yang Qi Duan Hongbo Fu Lin Gao Institute of Computing Technology CAS Beijing Key Laboratory of Mobile Computing and Pervasive Device School of Computer Science and Informatics Cardiff University UK SenseTime Research City University of Hong Kong Shenzhen Research Institute of Big Data Shenzhen

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

3D shape analysis is an important research topic in computer vision and graphics. While existing methods have generalized image-based deep learning to meshes using graph-based convolutions, the lack of an effective pooling operation restricts the learning capability of their networks. In this paper, we propose a novel pooling operation for mesh datasets with the same connectivity but different geometry, by building a mesh hierarchy using mesh simplification. For this purpose, we develop a modified mesh simplification method to avoid generating highly irregularly sized triangles. Our pooling operation effectively encodes the correspondence between coarser and finer meshes in the hierarchy. We then present a variational auto-encoder (VAE) structure with the edge contraction pooling and graph-based convolutions, to explore probability latent spaces of 3D surfaces and perform 3D shape generation. Our network requires far fewer parameters than the original mesh VAE and thus can handle denser models thanks to our new pooling operation and convolutional kernels. Our evaluation also shows that our method has better generalization ability and is more reliable in various applications, including shape generation and shape interpolation.

关键词： Three-dimensional displays Shape Strain Interpolation Neural networks Machine learning

Manipulating density of magnetic skyrmions via multilayer repetition and thermal annealing

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Physical Review B 2021年第6期104卷 064421-064421页

作者： Xinran Wang Anni Cao Sai Li Jin Tang Ao Du Houyi Cheng Yiming Sun Haifeng Du Xueying Zhang Weisheng Zhao Fert Beijing Institute School of Integrated Circuit Science and Engineering Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing 100191 China Beihang-Goertek Joint Microelectronics Institute Qingdao Research Institute Beihang University Qingdao 266000 China Beijing Microelectronics Technology Institute Beijing 100076 China Anhui Province Key Laboratory of Condensed Matter Physics at Extreme Conditions High Magnetic Field Laboratory of Chinese Academy of Sciences and University of Science and Technology of China Hefei 230031 China

The magnetic skyrmion, a tiny magnetic texture that holds promise as the next-generation information carrier, has been widely studied in recent years. A fine tunability of skyrmion density is required for its real applications in novel memory and logic devices. Here, we report on the manipulation of skyrmion density at room temperature in a Pt/Co/Ta/MgO system composed of multiple repetitions with perpendicular magnetic anisotropy. Néel-type skyrmions are observed by performing Lorentz transmission electron microscopy and magnetic force microscopy. The influence of structure repetition on skyrmions is experimentally investigated and further understood by micromagnetic simulations. The variation of skyrmion density with repetition number is mainly attributed to magnetic anisotropy. Meanwhile, thermal annealing is utilized to regulate the skyrmion density, where skyrmion-related properties exert a competitive effect. Our findings provide alternative means to manipulate skyrmion density, further allowing for optimized engineering of skyrmion-based devices.

关键词： Magnetic interactions Magnetic texture Skyrmions Spintronics Multilayer thin films Brillouin scattering & spectroscopy Lorentz microscopy Magnetic force microscopy

Insar Time-Series Deformation Forecasting Surrounding Salt Lake Using Deep Transformer Models

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SSRN

SSRN 2022年

作者： Wang, Jing Li, Chao Li, Lu Huang, Zhihua Wang, Chao Zhang, Hong Zhang, Zhengjia Research Institute of Intelligent Computing Zhejiang Laboratory. Hangzhou311121 China Key Laboratory of Digital Earth Science Aerospace Information Research Institute Chinese Academy of Sciences No.9 Dengzhuang South Road Beijing100094 China International Research Center of Big Data for Sustainable Development Goals Beijing100094 China University of Chinese Academy of Sciences Beijing100049 China Faculty of Information Engineering China University of Geosciences. 388 Lumo Road Wuhan430074 China

The open source strategy of Sentinel-1 SAR images enables Radar interferometry (InSAR) to perform time series surface deformation monitoring over large *** deformation monitoring and prediction can investigate the freeze-thaw cycles of permafrost on the Qinghai-Tibet Plateau (QTP). However, the convolutional and recurrent neural networks cannot accurately model long-term and complex relations in multi-variate time series data, it is challenging to implement time series deformation prediction with high spatial resolution. We propose an InSAR deformation prediction integrated algorithm based on the transformer-based models to predict time series deformation more accurately in permafrost areas. This method takes advantage of the self-attention mechanism to study complicated dynamic deformation features of permafrost caused by temperature and other variables from InSAR time series ***, Sentinel-1 dataset acquired with 12-day repeat are collected to implement the Small Baseline Subset (SBAS) method and retrieve the seasonal and long-term deformation surrounding Salt Lake. Then, the InSAR time series deformation and meteorological data are combined to establish the deformation prediction model using the transformer neural network. These results demonstrated that the InSAR deformation prediction method achieves better prediction performance (the root mean square errors are 3.207mm and 2.27mm) in predicting permafrost deformation trends with a point scale compared with the prediction results of other models. Finally, the predicted time series deformation maps for the period 2021/04/05 to 2022/03/19 are mapped surrounding Salt Lake. Meanwhile the total area of Salt Lake increased by 57.32km 2 during the period 2015-2019 based on the predicted deformation and the water extraction *** the expansion rate of Salt Lake was slow, and the area decreased by 3.26 km 2 from 2019 to 2022. The time series deformation forecasting method provides early warn

关键词： Deformation