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IEEE International Symposium on Circuits and Systems

作者： Ying Wang Lili Song Yinhe Han Yuanqing Cheng Huawei Li Xiaowei Li State Key Lab of Computer Architecture Institute of Computing technology Chinese Academy of Sciences Beihang University

ISBN: (纸本)9781479983926

Multi-level STT-RAM cell is able to boost the memory density at the expense of read/write reliability. However, the induced data integrity issue in STT-RAM memory can be effectively masked by a wide spectrum of applications with intrinsic forgiveness, which belong to the specific domain such as multimedia, synthesis and mining. In this work, we leverage the reconfigurable capability of MLC STT-RAM to provide variable-precision data storage for popular machine learning architectures. The targeted STT-RAM memory design is able to transform between multiple work modes and adaptable to meet the varying quality constraint of approximate applications. Particularly, we demonstrate the concept of precision-tunable STT-RAM memory with the emerging Convolution Neural Network accelerators and elaborate on the data mapping policy in STT-RAM memory to achieve the best energy-efficiency.

关键词： STT-RAM Machine Learning Neural Network

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Prevention from Soft Errors via architecture Elasticity

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Journal of computer Science & technology 2014年第2期29卷 247-254页

作者：尹一笑陈云霁郭崎陈天石 State Key Lab of Computer Architecture and Microprocessor Research Center Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Loongson Technology Corporation Limited IBM Research-China

Due to the decreasing threshold voltages, shrinking feature size, as well as the exponential growth of on-chip transistors, modern processors are increasingly vulnerable to soft errors. However, traditional mechanisms of soft error mitigation take actions to deal with soft errors only after they have been detected. Instead of the passive responses, this paper proposes a novel mechanism which proactively prevents from the occurrence of soft errors via architecture elasticity. In the light of a predictive model, we adapt the processor architectures h01istically and dynamically. The predictive model provides the ability to quickly and accurately predict the simulation target across different program execution phases on any architecture configurations by leveraging an artificial neural network model. Experimental results on SPEC CPU 2000 benchmarks show that our method inherently reduces the soft error rate by 33.2% and improves the energy efficiency by 18.3% as compared with the static configuration processor.

关键词： soft error energy efficiency architecture elasticity

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Dynamic Bayesian brain network partition and connectivity change point detection 5

Dynamic Bayesian brain network partition and connectivity ch...

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5th IEEE International Conference on Computational Advances in Bio and Medical Sciences, ICCABS 2015

作者： Lian, Zhichao Li, Xiang Pan, Yi Guo, Xuan Chen, Le Chen, Guantao Zhihui, Wei Liu, Tianming Zhang, Jing School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China Department of Mathematics and Statistics Georgia State University AtlantaGA United States Department of Statistics Yale University New HavenCT United States Cortical Architecture Imaging and Discovery Lab Department of Computer Science University of Georgia AthensGA United States Department of Computer Science Georgia State University AtlantaGA United States Department of Biology Georgia State University AtlantaGA United States

ISBN: (纸本)9781467396639

Multiple recent neuroimaging studies revealed that functional interactions within brain regions are locally clustered into small sub-networks where different dynamics of functional interaction occur. However, integration models investigating such functional brain dynamics have been rarely explored. In this paper, a novel Bayesian inference model is developed to partition the brain regions into different sub networks and to simultaneously segment temporal sequence of each sub network into several quasi-stable blocks based on the interaction dynamics among regions. The proposed model has been evaluated and validated by two different simulation models. Also, the model has been applied to a working-memory task-based fMRI dataset and interesting results on both dynamic sub networks and change points were obtained. © 2015 IEEE.

关键词： Dynamics

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BioTIME 2.0: Expanding and Improving a Database of Biodiversity Time Series

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Global Ecology and Biogeography 2025年第5期34卷

作者： Maria Dornelas Laura H. Antão Amanda E. Bates Viviana Brambilla Jonathan M. Chase Cher F. Y. Chow Ada Fontrodona-Eslava Anne E. Magurran Inês S. Martins Faye Moyes Alban Sagouis Samuel Adu-Acheampong Daniel Acquah-Lamptey Dušan Adam Penelope A. Ajani Aitor Albaina Pablo Almaraz Jeongseop An Roger Sigismund Anderson Madelaine Jean Robertson Anderson Alexsander Z. Antunes Ivan Arismendi Linda Armbrecht Pedro Aros-Mardones Sreejith Kalpuzha Ashtamoorthy Narayanan Ayyappan Gal Badihi Joseph J. Bailey Andrew H. Baird Mark Edward Baird Sreekumar Vadakkethil Balakrishnan José António L. Barão-Nóbrega Adi Barash Miguel Barbosa Jos Barlow Claus Bässler Matthieu Beaumont Natalie Beenaerts Tiago Octavio Begot Wallace Beiroz Ricardo Beldade David M. Bell Alecia Bellgrove Jonathan Belmaker Lisandro Benedetti-Cecchi Cassandra E. Benkwitt Pamela Medina-van Berkum Brandon T. Bestelmeyer Matthew G. Betts Maxwell Kelvin Billah Anne D. Bjorkman Magdalena Błażewicz Christopher P. Bloch Shane A. Blowes Antonio Bode Juliano A. Bogoni Thomas Bolger Timothy C. Bonebrake Erik Bonsdorff Roberta Bottarin Luke N. Brokensha Rob W. Brooker Andrew J. Brooks Helge Bruelheide Thiago Almeida Bueno Claire Laguionie Mariana Lopes Campagnoli James Cant Erica Pellegrini Caramaschi Alexandre Caron Tadhg Carroll Tancredi Caruso Juan Carvajal-Quintero Giuseppe Castaldelli Edward Castañeda-Moya Pedro V. Castilho Sonia Zanini Cechin Shahar Chaikin Uchangi Manjunatha Chandrashekara Tory J. Chase Chaolun Allen Chen Jorge José Cherem Sei-Woong Choi Erica M. Christensen Alexander V. Christianini Jackson Wing Four Chu Peter Coad Carl Van Colen Lise Comte Elisabeth J. Cooper J. Hans C. Cornelissen Eddy Cosson Unai Cotano Luc Crevecoeur Shannan Kyle Crow Graeme S. Cumming Vanessa S. Daga Gabriella Damasceno Gergana N. Daskalova Claire H. Davies Robert A. Davis Frank P. Day Sussy De-La-Zerda Amy Elizabeth Deacon Indradatta de Castro-Arrazola Steven Degraer Kharran Deonarinesingh Juan C. Diaz-Ricaurte Christopher R. Dickman Tara Dirilgen Ciaran John Dolan J. Emmet Centre for Biological Diversity School of Biology University of St Andrews Fife UK MARE—Centro de Ciências do Mar e do Ambiente Faculdade de Ciências Universidade de Lisboa Lisboa Portugal Research Centre for Ecological Change Organismal and Evolutionary Biology Research Programme Faculty of Biological and Environmental Sciences University of Helsinki Helsinki Finland Department of Biology University of Victoria Victoria British Columbia Canada German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig Leipzig Germany Department of Computer Science Martin Luther University Halle-Wittenberg Halle (Saale) Germany Leverhulme Centre for Anthropocene Biodiversity Department of Biology University of York York UK Department of Animal Biology and Conservation Science University of Ghana Accra Ghana Philipps Universität Marburg Marburg Germany Department of Forest Ecology Silva Tarouca Research Institute Brno Czech Republic School of Life Sciences University of Technology Sydney Sydney New South Wales Australia Zoology and Animal Cell Biology Department University of the Basque Country (UPV/EHU) Leioa Spain Grupo de Oceanografía de Ecosistemas Instituto de Ciencias Marinas de Andalucía CSIC Campus Universitario de Puerto Real Puerto Real Spain National Institute of Ecology Seocheon Republic of Korea African Regional Postgraduate Programme in Insect Science University of Ghana Accra Ghana Département de Biologie Université de Sherbrooke Sherbrooke Quebec Canada Núcleo de Conservação da Biodiversidade Instituto de Pesquisas Ambientais Secretaria de Meio Ambiente Infraestrutura e Logística do Estado de São Paulo São Paulo Brazil Department of Fisheries Wildlife and Conservation Sciences Oregon State University Corvallis Oregon USA Institute for Marine and Antarctic Studies Ecology & Biodiversity Centre University of Tasmania Hobart Tasmania Australia Graduate Program in Oceanography Department of Oceanography Faculty of Natural Scienc

Motivation Here, we make available a second version of the BioTIME database, which compiles records of abundance estimates for species in sample events of ecological assemblages through time. The updated version expands version 1.0 of the database by doubling the number of studies and includes substantial additional curation to the taxonomic accuracy of the records, as well as the metadata. Moreover, we now provide an R package (BioTIMEr) to facilitate use of the database. Main Types of Variables Included The database is composed of one main data table containing the abundance records and 11 metadata tables. The data are organised in a hierarchy of scales where 11,989,233 records are nested in 1,603,067 sample events, from 553,253 sampling locations, which are nested in 708 studies. A study is defined as a sampling methodology applied to an assemblage for a minimum of 2 years. Spatial Location and Grain Sampling locations in BioTIME are distributed across the planet, including marine, terrestrial and freshwater realms. Spatial grain size and extent vary across studies depending on sampling methodology. We recommend gridding of sampling locations into areas of consistent size. Time Period and Grain The earliest time series in BioTIME start in 1874, and the most recent records are from 2023. Temporal grain and duration vary across studies. We recommend doing sample-level rarefaction to ensure consistent sampling effort through time before calculating any diversity metric. Major Taxa and Level of Measurement The database includes any eukaryotic taxa, with a combined total of 56,400 taxa. Software Format csv and. SQL.

关键词： assemblage biodiversity community database ecoinformatics species taxa time

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26th Annual Computational Neuroscience Meeting (CNS*2017) of the Organization for Computational Neuroscience Antwerp, Belgium, July 15-20, 2017

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BMC NEUROSCIENCE 2017年第SUPPL 1期18卷 59-59页

作者： [Anonymous] Indiana University Purdue University Indianapolis Indianapolis IN 46032 USA Stark Neurosciences Research Institute Indiana University School of Medicine Indianapolis IN 46032 USA Department of Mathematics East Carolina University Greenville NC 27858 USA Jülich Supercomputing Centre Forschungszentrum Jülich 52425 Jülich Germany Future Systems Swiss National Supercomputing Centre 8092 Zurich Switzerland User Engagement and Support Swiss National Supercomputing Centre 6900 Lugano Switzerland Institut de Neurosciences des Systèmes Aix Marseille Univ 13005 Marseille France Simulation Lab Neuroscience Forschungszentrum Jülich Jülich Germany Department of Experimental Psychology Ghent University 9000 Ghent Belgium Donders Center for Cognitive Neuroimaging Radboud University 6525HR Nijmegen The Netherlands Department of Electrical Computer and Energy Engineering University of Colorado Boulder CO 80309 USA Department of Neurosurgery Johns Hopkins School of Medicine Baltimore MD 21287 USA Department of Neurology Johns Hopkins School of Medicine Baltimore MD 21287 USA Department of Otolaryngology Johns Hopkins School of Medicine Baltimore MD 21287 USA INSERM U968 Paris France Sorbonne Universités UPMC University Paris 06 UMR_S 968 Institut de la Vision Paris France CNRS UMR_7210 Paris France Department of Computer Architecture and Technology University of Granada (CITIC) Granada Spain Sorbonne Universités UPMC Univ Paris 06 INSERM CNRS Institut de la Vision Paris France Department of Adaptive Machine Systems Osaka University Osaka Japan Department of Computer Science University of Cergy-Pontoise Cergy-Pontoise France Department of Physics and Astronomy College of Charleston Charleston SC 29424 USA School of Physics Faculty of Science University of Sydney Sydney NSW 2006 Australia Center of Excellence for Integrative Brain Function Australian Research Council Sydney Australia Max Planck Institute for Human Cognitive and Brain Sciences Saxony Lei

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Multiscale and multimodal fusion of tract-tracing and DTI-derived fibers in macaque brains

Multiscale and multimodal fusion of tract-tracing and DTI-de...

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IEEE International Symposium on Biomedical Imaging

作者： Ke Jing Tuo Zhang Jianfeng Lu Hanbo Chen Xi Jiang Lei Guo Longchuan Li Xiaoping Hu Jinglei Lv Bao Ge Tianming Liu Cortical Architecture Imaging and Discovery Lab The University of Georgia Athens GA School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China School of Automation Northwestern Polytechnical University Xi'an China Biomedical Imaging Technology Center Emory University Atlanta GA School of Physics & Information Technology Shaanxi Normal University Xi'an China

ISBN: (纸本)9781467393300

Assessment of structural connectivity patterns of macaque brains may hold the key to understanding mechanism of cortical convolution and brain function. Therefore, lots of interests have been attracted to analyze axonal pathways via up-to-date techniques, such as tract-tracing data, which is taken as `gold standard' to estimate trustworthy meso-scale pathways and diffusion MRI (e.g., DTI), from which macroscale brain connectivity map can be reconstructed. In this paper, we for the first time propose a framework to take advantages of the two modalities to identify cross-validated connections and construct corresponding dMRI fiber bundle atlas. This framework is conducted on a whole-brain-connectivity base by fusing information from dMRI derived connectivity maps and tract-tracing connectivity maps derived from CoCoMac database, in which tract-tracing reports are collated across a large research community and the tract-tracing connectivity maps are inferred in a meta-analysis fashion. We demonstrate the effectiveness of the framework by a variety of experiments.

关键词： Diffusion tensor imaging Optical fiber testing Optical fiber theory Joints Indexes

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Parallel optimization of geometric correction algorithm based on CPU-GPU hybrid architecture

Parallel optimization of geometric correction algorithm base...

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International Conference on Vehicle and Mechanical Engineering and Information technology, VMEIT 2014

作者： Bai, Hong Tao Li, Yu Gang Chen, Li Ying Wang, Yan Ling Beijing Lab of Intelligent Information Technology School of Computer Science Beijing Institute of Technology Beijing 100081 China Beijing Lab of Intelligent Information Technology School of Computer Science Beijing Institute of Technology Beijing China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing China

ISBN: (纸本)9783038350606

Geometric correction is an essential processing procedure in remote sensing image processing. The algorithms used in geometric correction are time intensive and the size of remote sensing images is very large. Meanwhile, the data to be calculated is in huge size and is accumulating rapidly every day. Hence, the fast processing of geometric correction of remote sensing image becomes an urgent research problem. Through the rapid development of GPU, the current GPU has a great advantage in processing speed and memory bandwidth over CPU. It provides a new way for high performance computing. In this paper, we present three optimization solutions based on CPU-GPU hybrid architecture and the analysis of their performances. Experiments are also given and the results are consistent with the analysis. © (2014) Trans Tech Publications, Switzerland.

关键词： Graphics processing unit

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A hop-by-hop energy efficient distributed routing cheme

A hop-by-hop energy efficient distributed routing cheme

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作者： Hou, Chenying Zhang, Fa Anta, Antonio Fernández Wang, Lin Liu, Zhiyong Institute of Computing Technology CAS University of Chinese Academy of Sciences Beijing China Institute IMDEA Networks Madrid Spain Institute of Computing Technology Chinese State Key Lab for Computer Architecture CAS Beijing China

Energy inefficiencies in current networks provide both challenges and opportunities for energy saving. Recently, there are many works focusing on minimizing energy cost from the routing perspective. However, most existing work view them as optimization problems and solve them in a centralized manner such as with a solver or using approximations. In this paper, we focus on a network-wide bi-objective optimization problem, which simultaneously minimizes the total energy consumption using speed scaling and the total traffic delay. We propose a hop-by-hop dynamic distributed routing scheme for the implementation of this network-wide optimization problem. Our scheme is more practical to realize in large distributed networks compared with current centralized energy minimization methods. We can also achieve near global optimal in a distributed manner, while most used shortest path routing protocols such as OSPF cannot make it. Our routing scheme is totally distributed and maintains loop-free routes in every instant. Simulations conducted in real data sets show that the distributed loop-free routing scheme converges to near Pareto optimal values. Also, our method outperforms the widely applied shortest path routing strategy by 30% in energy saving.

关键词： Energy utilization

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What's in the Container? Classifying Object Contents from Vision and Touch

What's in the Container? Classifying Object Contents from Vi...

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IEEE/RSJ International Conference on Intelligent Robots and Systems

作者： Puren Guler Yasemin Bekiroglu Xavi Gratal Karl Pauwels Danica Kragic Computer Vision and Active Perception Lab Center for Autonomous Systems School of Computer Science and Communication KTH Royal Institute of Technology Stockholm Sweden Computer Architecture and Technology Department University of Granada Spain

ISBN: (纸本)9781479969357

Robots operating in household environments need to interact with food containers of different types. Whether a container is filled with milk, juice, yogurt or coffee may affect the way robots grasp and manipulate the container. In this paper, we concentrate on the problem of identifying what kind of content is in a container based on tactile and/or visual feedback in combination with grasping. In particular, we investigate the benefits of using unimodal (visual or tactile) or bimodal (visual-tactile) sensory data for this purpose. We direct our study toward cardboard containers with liquid or solid content or being empty. The motivation for using grasping rather than shaking is that we want to investigate the content prior to applying manipulation actions to a container. Our results show that we achieve comparable classification rates with unimodal data and that the visual and tactile data are complimentary.

关键词： CONTAINERS Grasp Robot Food Containers dry matter content NONVOLATILE CONTENT Visualization Unimodal

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Data-aware DRAM refresh to squeeze the margin of retention time in hybrid memory cube 14

Data-aware DRAM refresh to squeeze the margin of retention t...

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Proceedings of the 2014 IEEE/ACM International Conference on computer-Aided Design

作者： Yinhe Han Ying Wang Huawei Li Xiaowei Li State Key Lab of Computer Architecture Institute of Computing technology Chinese Academy of Sciences and University of Chinese Academy of Sciences

ISBN: (纸本)9781479962778

With the increase of storage density, DRAM refresh leads to higher overhead of power and bandwidth, particularly in emerging 3D stacked memory design like Hybrid Memory Cube (HMC). To exploit the hardware resources for a smarter solution, we propose a data-aware refresh control scheme, Trial and Error (Trial-n-Error), which leverages the data-pattern dependence characteristics of the cells' retention time to reduce refresh operations. Trial-n-Error is a systematic approach that employs our proposed Synergy Testing to capture the refresh bottleneck of DRAM memory: "weak" cells that have a relatively shorter retention time. By locating the dominant weak cells sensitized by applications, Trial-n-Error can avoid the worst-case refresh setting, and adjust the refresh rate under the control of our self-tuning algorithm. Thus, Trial-n-Error can gradually approach to the possible lower-bound of refresh rate for less energy and memory bandwidth consumption. In experiments of 3D-stacked DRAMs, we successfully eliminate an average of 28% refresh operations and save 21% refresh energy for a set of pre-profiled synthetic data patterns and real benchmarks.

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