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Course Description


Asim Roy
(Arizona State University) [intermediate]
Hardware-based (GPU, FPGA based) Machine Learning That Exploits Massively Parallel Computing – An Overview of Concepts, Architectures and Neural Network Algorithm Implementation

Summary

Deep learning owes its success to the advent of massively parallel computing enabled by FPGAs (Field Programmable Gate Arrays), GPUs (Graphical Processing Units) and other special processors. However, many other neural network architectures can exploit such massively parallel computing. In this course, I will introduce the basic concepts and architectures of heterogeneous computing using FPGAs and GPUs. There are two basic languages for programming such hardware – OpenCL for FPGAs (from Intel, Xilinx and others) and CUDA for Nvidia GPUs. I will introduce the basic features of these languages and show how to implement parallel computations in these languages.

In the second part of this course, I will show how to implement some basic neural architectures on this kind of hardware. In addition, we can do much more with such hardware including feature selection, hyperparameter tuning and finding a good neural architecture. Finding the best combination of features, the best neural network design and the best hyperparameters is critical to neural networks. With the availability of massive parallelism, it is relatively easy now to explore, in parallel, many different combinations of features, neural network designs and hyperparameters.

In the last part of the course, I will discuss why it is becoming important that machine learning for IoT be at the edge of IoT instead of the cloud and how FPGAs and GPUs can facilitate that. And not just IoT, but in a wide range of application domains, from robotics to remote patient monitoring, localized machine learning from streaming sensor data is becoming increasingly important. GPUs, in particular, are available in a wide range of capabilities and prices and one can use them in many such applications where localized machine learning is desirable.

Syllabus

  • ►Lecture 1: Massively parallel, heterogeneous computing using FPGAs and GPUs – heterogeneous computing concepts and architectures; comparison of FPGAs and GPUs; programming languages for parallel computing (OpenCL, CUDA)

  • ►Lecture 2: Implementation of basic neural network algorithms on FPGAs and GPUs exploiting massive parallelism; exploiting massive parallelism to explore different feature combinations and neural network designs and for hyperparameter tuning

  • ►Lecture 3: Machine learning at the edge of IoT in real-time from streaming sensor data using FPGAs and GPUs – classification, function approximation, clustering, anomaly detection

References

  • ► 1. Du, P., Weber, R., Luszczek, P., Tomov, S., Peterson, G., & Dongarra, J. (2012). From CUDA to OpenCL: Towards a performance-portable solution for multi-platform GPU programming. Parallel Computing, 38(8), 391-407.
  • ► 2. Karimi, K., Dickson, N. G., & Hamze, F. (2010). A performance comparison of CUDA and OpenCL. arXiv preprint arXiv:1005.2581.
  • ► 3. Lacey, G., Taylor, G. W., & Areibi, S. (2016). Deep learning on fpgas: Past, present, and future. arXiv preprint arXiv:1602.04283.
  • ► 4. Li, H., Ota, K., & Dong, M. (2018). Learning IoT in edge: Deep learning for the Internet of Things with edge computing. IEEE Network, 32(1), 96-101.
  • ► 5. Martinez, G., Gardner, M., & Feng, W. C. (2011, December). CU2CL: A CUDA-to-OpenCL translator for multi-and many-core architectures. In 2011 IEEE 17th International Conference on Parallel and Distributed Systems (pp. 300-307). IEEE.
  • ► 6. Misra, J., & Saha, I. (2010). Artificial neural networks in hardware: A survey of two decades of progress. Neurocomputing, 74(1-3), 239-255.
  • ► 7. Nurvitadhi, E., Venkatesh, G., Sim, J., Marr, D., Huang, R., Ong Gee Hock, J., ... & Boudoukh, G. (2017, February). Can FPGAs beat GPUs in accelerating next-generation deep neural networks?. In Proceedings of the 2017 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays (pp. 5-14). ACM.
  • ► 8. Oh, K. S., & Jung, K. (2004). GPU implementation of neural networks. Pattern Recognition, 37(6), 1311-1314.
  • ► 9. Omondi, A. R., & Rajapakse, J. C. (Eds.). (2006). FPGA implementations of neural networks (Vol. 365, p. 6). Dordrecht, The Netherlands: Springer.
  • ► 10. Ortega-Zamorano, F., Jerez, J. M., Munoz, D. U., Luque-Baena, R. M., & Franco, L. (2015). Efficient implementation of the backpropagation algorithm in FPGAs and microcontrollers. IEEE transactions on neural networks and learning systems, 27(9), 1840-1850.
  • ► 11. Verhelst, M., & Moons, B. (2017). Embedded deep neural network processing: Algorithmic and processor techniques bring deep learning to iot and edge devices. IEEE Solid-State Circuits Magazine, 9(4), 55-65.
  • ► 12. Zhu, J., & Sutton, P. (2003, September). FPGA implementations of neural networks–a survey of a decade of progress. In International Conference on Field Programmable Logic and Applications (pp. 1062-1066). Springer, Berlin, Heidelberg.

Pre-requisites

Fundamentals of computer science, basic knowledge of neural networks

Short Bio

Asim Roy is a professor of information systems at Arizona State University. He earned his bachelor's degree from Calcutta University, his master's degree from Case Western Reserve University, and his doctorate from the University of Texas at Austin. He has been a visiting scholar at Stanford University and a visiting scientist at the Robotics and Intelligent Systems Group at Oak Ridge National Laboratory, Tennessee. Professor Roy serves on the Governing Board of the International Neural Network Society (INNS) and is currently its VP of Industrial Relations. He is the founder of two INNS Sections, one on Autonomous Machine Learning and the other on Big Data Analytics. He was the Guest Editor-in-Chief of an open access eBook Representation in the Brain of Frontiers in Psychology. He was also the Guest Editor-in-Chief of two special issues of Neural Networks - one on autonomous learning and the other on big data analytics. He is the Senior Editor of Big Data Analytics and serves on the editorial boards of Neural Networks and Cognitive Computation.

He has served on the organizing committees of many scientific conferences. He started the Big Data conference series of INNS and was the General Co-Chair of the first one in San Francisco in 2015. He was the Technical Program Co-Chair of IJCNN 2015 in Ireland and the IJCNN Technical Program Co-Chair for the World Congress on Computational Intelligence 2018 (WCCI 2018) in Rio de Janeiro, Brazil. He is currently the IJCNN General Chair for WCCI 2020 in Glasgow, UK (https://www.wcci2020.org/). He is currently working on hardware-based (GPU, FPGA-based) machine learning for real-time learning from streaming data at the edge of the Internet of Things (IoT). He is also working on Explainable AI.



Hanan Samet
(University of Maryland) [introductory/intermediate]
Sorting in Space: Multidimensional, Spatial, and Metric Data Structures for Applications in Spatial and Spatio-textual Databases, Geographic Information Systems (GIS), and Location-based Services

Summary

The representation of multidimensional, spatial, and metric data is an important issue in applications of spatial and spatiotextual databases, geographic information systems (GIS), and location-based services. Recently, there has been much interest in hierarchical data structures such as quadtrees, octrees, and pyramids which are based on image hierarchies, as well methods that make use of bounding boxes which are based on object hierarchies. Their key advantage is that they provide a way to index into space. In fact, they are little more than multidimensional sorts. They are compact and depending on the nature of the spatial data they save space as well as time and also facilitate operations such as search.

We describe hierarchical representations of points, lines, collections of small rectangles, regions, surfaces, and volumes. For region data, we point out the dimension-reduction property of the region quadtree and octree. We also demonstrate how to use them for both raster and vector data. For metric data that does not lie in a vector space so that indexing is based simply on the distance between objects, we review various representations such as the vp-tree, gh-tree, and mb-tree. In particular, we demonstrate the close relationship between these representations and those designed for a vector space.
For all of the representations, we show how they can be used to compute nearest objects in an incremental fashion so that the number of objects need not be known in advance. The VASCO JAVA applet is presented that illustrates these methods (found at http://www.cs.umd.edu/~hjs/quadtree/index.html). They are also used in applications such as the SAND Internet Browser (found at http://www.cs.umd.edu/~brabec/sandjava).

The above has been in the context of the traditional geometric representation of spatial data, while in the final part we review the more recent textual representation which is used in location-based services where the key issue is that of resolving ambiguities. For example, does London'' is it. The NewsStand system at newsstand.umiacs.umd.edu and the TwitterStand system at TwitterStand.umiacs.umd.edu system are examples. See also the cover article of the October 2014 issue of Communications of the ACM at http://tinyurl.com/newsstand-cacm or a cached version at http://www.cs.umd.edu/~hjs/pubs/cacm-newsstand.pdf and the accompanying video at https://vimeo.com/106352925

Syllabus

    1. Introduction
  • a. Sample queries
  • b. Spatial Indexing
  • c. Sorting approach
  • d. Minimum bounding rectangles (e.g., R-tree)
  • e. Disjoint cells (e.g., R+-tree, k-d-B-tree)
  • f. Uniform grid
  • g. Location-based queries vs: feature-based queries
  • h. Region quadtree
  • i. Dimension reduction
  • j. Pyramid
  • k. Region quadtrees vs: pyramids
  • l. Space ordering methods
    1. Points
  • a. point quadtree
  • b. MX quadtree
  • c. PR quadtree
  • d. k-d tree
  • e. Bintree
  • f. BSP tree
    1. Lines
  • a. Strip tree
  • b. PM1 quadtree
  • c. PM2 quadtree
  • d. PM3 quadtree
  • e. PMR quadtree
    1. Rectangles and arbitrary objects
  • a. MX-CIF quadtree
  • b. Loose quadtree
  • c. Partition fieldtree
  • d. R-tree
    1. Surfaces and Volumes
  • a. Restricted quadtree
  • b. Region octree
  • c. PM octree
    1. Metric Data
  • a. vp-tree
  • b. gh-tree
  • c. mb-tree
    1. Operations
  • a. Incremental nearest object location
  • b. Boolean set operations
    1. Spatial Database Issues
  • a. General issues
  • b. Specific issues
    1. Indexing for spatiotextual databases and location-based services delivered on platforms such as smart phones and tablets
  • a. Incorporation of spatial synonyms in search engines
  • b. Toponym recognition
  • c. Toponym resolution
  • d. Spatial reader scope
  • e. Incorporation of spatiotemporal data
  • f. System integration issues
    1. Example systems
  • a. SAND internet browser
  • b. JAVA spatial data applets
  • c. STEWARD
  • d. NewsStand on a smartphone
  • e. TwitterStand

References

    1. H. Samet. ``Foundations of Multidimensional Data Structures.'' Morgan-Kaufmann, San Francisco, 2006.
    1. H. Samet. ``A sorting approach to indexing spatial data.'' International Journal of Shape Modeling. 14(1):15--37, 28(4):517--580, June 2008.
    1. G. R. Hjaltason and H. Samet. ``Index-driven similarity search in metric spaces.'' ACM Transactions on Database Systems, 28(4):517--580, December 2003.
    1. G. R. Hjaltason and H. Samet. ``Distance browsing in spatial databases.'' ACM Transactions on Database Systems, 24(2):265--318, June 1999. Also Computer Science TR-3919, University of Maryland, College Park, MD.
    1. G. R. Hjaltason and H. Samet. ``Ranking in spatial databases.'' In Advances in Spatial Databases --- 4th International Symposium, SSD'95, M. J. Egenhofer and J. R. Herring, eds., Portland, ME, August 1995, 83--95. Also Springer-Verlag Lecture Notes in Computer Science
      1. H. Samet. ``Applications of Spatial Data Structures: Computer Graphics, Image Processing, and GIS.'' Addison-Wesley, Reading, MA,
        1. H. Samet. ``The Design and Analysis of Spatial Data Structures.'' Addison-Wesley, Reading, MA, 1990.
        1. C. Esperanca and H. Samet. ``Experience with SAND/Tcl: a scripting tool for spatial databases.'' Journal of Visual Languages and Computing, 13(2):229--255, April 2002.
        1. H. Samet, H. Alborzi, F. Brabec, C. Esperanca, G. R. Hjaltason, F. Morgan, and E. Tanin. ``Use of the SAND spatial browser for digital government applications.'' Communications of the ACM, 46(1):63--66, January 2003.
        1. B. Teitler, M. D. Lieberman, D. Panozzo, J. Sankaranarayanan, H. Samet, and J. Sperling. ``NewsStand: A new view on news.'' Proceedings of the 16th ACM SIGSPATIAL International Conference o n Advances in Geographic Information Systems, Irvine, CA, November 2008, 144--153. SIGSPATIAL 10-Year Impact Award.
        1. H. Samet, J. Sankaranarayanan, M. D. Lieberman, M. D. Adelfio, B. C. Fruin, J. M. Lotkowski, D. Panozzo, J. Sperling, and B. E. Teitler. ``Reading news with maps by exploiting spatial synonyms.'' Communications of the ACM, 57(10):64--77, October 2014.
        1. J. Sankaranarayanan, H. Samet, B. Teitler, M. D. Lieberman, and J. Sperling. ``TwitterStand: News in tweets.'' Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, Seattle, WA, November 2009, 42--51.
        1. M. D. Lieberman, H. Samet, and J. Sankaranarayanan. ``Geotagging with local lexicons to build indexes for textually-specified spatial data.'' Proceedings of the 26th IEEE International Conference on Data Engineering, Long Beach, CA, March 2010, 201--212.
        1. M. D. Lieberman and H. Samet. ``Multifaceted Toponym Recognition for Streaming News.'' Proceedings of the ACM SIGIR Conference. Beijing, July 2011, 843--852.
        1. M. D. Lieberman and H. Samet. ``Adaptive Context Features for Toponym Resolution in Streaming News.'' Proceedings of the ACM SIGIR Conference. Portland, OR, August 2012, 731--740.
        1. M. D. Lieberman and H. Samet. Supporting Rapid Processing and Interactive Map-Based Exploration of Streaming News. Proceedings of the 20th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. Redondo Beach, CA, November 2012, 179--188/
        1. H. Samet, B. C. Fruin, and S. Nutanong. Duking it out at the smartphone mobile app mapping API corral: Apple, Google, and the competition. In Proceedings of the 1st ACM SIGSPATIAL International Workshop on Mobile Geographic Information Systems (MobiGIS 2012), Redondo Beach, CA, November 2012.
        1. H. Samet, S. Nutanong, and B. C. Fruin. Dynamic presentation consistency issues in smartphone mapping apps. Communications of the ACM, 59(9):58--67, September 2016.
        1. H. Samet, S. Nutanong, and B. C. Fruin. Static presentation consistency issues in smartphone mapping apps. Communications of the ACM, 59(5):88--98, May 2016.
        1. G. Quercini, H. Samet, J. Sankaranarayanan, and M.D. Lieberman. Determining the spatial reader scopes of news sources using local lexicons. In Proceedings of the 18th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, San Jose, CA, November 2010, 43--52,
        1. Spatial Data Structure applets at; http://www.cs.umd.edu/~hjs/quadtree/index.html.

      Pre-requisites

      Practitioners working in the areas of big spatial data and spatial data science that involve spatial databases, geographic information systems, and location-based services will be given a different perspective on data structures found to be useful in most applications. Familiarity with computer terminology and some programming experience is needed to follow this course.

      Short Bio

      Hanan Samet (http://www.cs.umd.edu/~hjs/) is a Distinguished University Professor of Computer Science at the University of Maryland, College Park and is a member of the Institute for Computer Studies. He is also a member of the Computer Vision Laboratory at the Center for Automation Research where he leads a number of research projects on the use of hierarchical data structures for database applications, geographic information systems, computer graphics, computer vision, image processing, games, robotics, and search. He received the B.S. degree in engineering from UCLA, and the M.S. Degree in operations research and the M.S. and Ph.D. degrees in computer science from Stanford University. His doctoral dissertation dealt with proving the correctness of translations of LISP programs which was the first work in translation validation and the related concept of proof-carrying code.
      He is the author of the recent book

      Источник: https://bigdat2020.irdta.eu/coursedescription/
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