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Cost-effective, Energy-efficient, and Scalable Storage Computing for Large-scale AI Applications

Published:12 October 2020Publication History
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Abstract

The growing volume of data produced continuously in the Cloud and at the Edge poses significant challenges for large-scale AI applications to extract and learn useful information from the data in a timely and efficient way. The goal of this article is to explore the use of computational storage to address such challenges by distributed near-data processing. We describe Newport, a high-performance and energy-efficient computational storage developed for realizing the full potential of in-storage processing. To the best of our knowledge, Newport is the first commodity SSD that can be configured to run a server-like operating system, greatly minimizing the effort for creating and maintaining applications running inside the storage. We analyze the benefits of using Newport by running complex AI applications such as image similarity search and object tracking on a large visual dataset. The results demonstrate that data-intensive AI workloads can be efficiently parallelized and offloaded, even to a small set of Newport drives with significant performance gains and energy savings. In addition, we introduce a comprehensive taxonomy of existing computational storage solutions together with a realistic cost analysis for high-volume production, giving a good big picture of the economic feasibility of the computational storage technology.

References

  1. Sami Abu-El-Haija, Nisarg Kothari, Joonseok Lee, Paul Natsev, George Toderici, Balakrishnan Varadarajan, and Sudheendra Vijayanarasimhan. 2016. Youtube-8m: A large-scale video classification benchmark. Arxiv Preprint Arxiv:1609.08675 (2016).Google ScholarGoogle Scholar
  2. I. Aleksander, M. De Gregorio, F. Maia Galvão França, P. Machado Vieira Lima, and H. Morton. 2009. A brief introduction to weightless neural systems. In Proceedings of the 17th European Symposium on Artificial Neural Networks. Retrieved from https://www.elen.ucl.ac.be/Proceedings/esann/esannpdf/es2009-6.pdf.Google ScholarGoogle Scholar
  3. I. Aleksander, W. V. Thomas, and P. A. Bowden. 1984. WISARDa radical step forward in image recognition. Sensor Rev. 4, 3 (1984), 120--124. DOI:https://doi.org/10.1108/eb007637Google ScholarGoogle ScholarCross RefCross Ref
  4. Martin Aumüller, Erik Bernhardsson, and Alexander Faithfull. 2020. ANN-benchmarks: A benchmarking tool for approximate nearest neighbor algorithms. Inf. Syst. 87 (2020), 101374.Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Doug Beaver, Sanjeev Kumar, Harry C. Li, Jason Sobel, Peter Vajgel et al. 2010. Finding a needle in haystack: Facebook’s photo storage. In Proceedings of the Symposium on Operating Systems Design and Implementation, Vol. 10. 1--8.Google ScholarGoogle Scholar
  6. D. S. Bolme. 2010. Visual object tracking using adaptive correlation filters. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.Google ScholarGoogle ScholarCross RefCross Ref
  7. Ali Borji, Ming-Ming Cheng, Qibin Hou, Huaizu Jiang, and Jia Li. 2019. Salient object detection: A survey. Comput. Vis. Media (2019), 1--34.Google ScholarGoogle Scholar
  8. Neil Briscoe. 2000. Understanding the OSI 7-layer model. PC Netw. Advis. 120, 2 (2000).Google ScholarGoogle Scholar
  9. Li-Pin Chang, Tei-Wei Kuo, and Shi-Wu Lo. 2004. Real-time garbage collection for flash-memory storage systems of real-time embedded systems. ACM Trans. Embed. Comput. Syst. 3, 4 (2004), 837--863.Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. Sangyeun Cho, Chanik Park, Hyunok Oh, Sungchan Kim, Youngmin Yi, and Gregory R. Ganger. 2013. Active disk meets flash: A case for intelligent SSDs. In Proceedings of the 27th International ACM Conference on International Conference on Supercomputing. ACM, 91--102.Google ScholarGoogle Scholar
  11. Tae-Sun Chung, Dong-Joo Park, Sangwon Park, Dong-Ho Lee, Sang-Won Lee, and Ha-Joo Song. 2009. A survey of flash translation layer. J. Syst. Archit. 55, 5–6 (2009), 332--343.Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. Michael Cornwell. 2012. Anatomy of a solid-state drive.Commun. ACM 55, 12 (2012), 59--63.Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. Leonardo Dagum and Ramesh Menon. 1998. OpenMP: An industry-standard API for shared-memory programming. Comput. Sci. Eng.1 (1998), 46--55.Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. Trevor Darrell, Piotr Indyk, and Gregory Shakhnarovich. 2005. Nearest-neighbor Methods in Learning and Vision: Theory and Practice. The MIT Press.Google ScholarGoogle Scholar
  15. Jaeyoung Do, Yang-Suk Kee, Jignesh M. Patel, Chanik Park, Kwanghyun Park, and David J. DeWitt. 2013. Query processing on smart SSDs: Opportunities and challenges. In Proceedings of the ACM SIGMOD International Conference on Management of Data. ACM, 1221--1230.Google ScholarGoogle Scholar
  16. Jaeyoung Do, Sudipta Sengupta, and Steven Swanson. 2019. Programmable solid-state storage in future cloud datacenters. Commun. ACM 62, 6 (2019), 54--62.Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. Daniel N. Do Nascimiento, Rafael Lima De Carvalho, Felix Mora-Camino, Priscila V. M. Lima, and Felipe M. G. Franca. 2015. A WiSARD-based multi-term memory framework for online tracking of objects. In Proceedings of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. 978–287587014–8. DOI:https://doi.org/10.13140/RG.2.1.3387.5687Google ScholarGoogle Scholar
  18. Carlotta Domeniconi, Jing Peng, and Dimitrios Gunopulos. 2002. Locally adaptive metric nearest-neighbor classification. IEEE Trans. Pattern Anal. Mach. Intell. 24, 9 (2002), 1281--1285.Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. Eideticom. 2020. Retrieved from https://www.eideticom.com/.Google ScholarGoogle Scholar
  20. K. Eshghi and Rino Micheloni. 2013. SSD architecture and PCI express interface. In Inside Solid State Drives (SSDs). Springer, 19--45.Google ScholarGoogle Scholar
  21. A. E. Eshratifar, M. S. Abrishami, and M. Pedram. 2019. JointDNN: An efficient training and inference engine for intelligent mobile cloud computing services. IEEE Trans. Mob. Comput. (2019), 1--1.Google ScholarGoogle Scholar
  22. Mark Fasheh. 2006. OCFS2: The Oracle Clustered File System, version 2. In Proceedings of the Linux Symposium, Vol. 1. 289--302.Google ScholarGoogle Scholar
  23. Horacio L. França, João Carlos P. da Silva, Omar Lengerke, Max Suell Dutra, Massimo De Gregorio, and Felipe Maia Galvão França. 2010. Movement persuit control of an offshore automated platform via a RAM-based neural network. In Proceedings of the 11th International Conference on Control Automation Robotics 8 Vision. 2437--2441.Google ScholarGoogle ScholarCross RefCross Ref
  24. John Gantz and David Reinsel. 2012. The digital universe in 2020: Big data, bigger digital shadows, and biggest growth in the Far East. IDC iView: IDC Analyze the future 2007, 2012 (2012), 1--16.Google ScholarGoogle Scholar
  25. Richard L. Graham, Timothy S. Woodall, and Jeffrey M. Squyres. 2006. Open MPI: A flexible high performance MPI. In Parallel Processing and Applied Mathematics, Roman Wyrzykowski, Jack Dongarra, Norbert Meyer, and Jerzy Waśniewski (Eds.). Springer Berlin, 228--239.Google ScholarGoogle Scholar
  26. William Gropp, Ewing Lusk, Nathan Doss, and Anthony Skjellum. 1996. A high-performance, portable implementation of the MPI message passing interface standard. Parallel Comput. 22, 6 (1996), 789--828.Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. SSD Form Factor Working Group. 2011. Retrieved from http://www.ssdformfactor.org/docs/SSD_Form_Factor_Version1_00.pdf.Google ScholarGoogle Scholar
  28. Boncheol Gu, Andre S. Yoon, Duck-Ho Bae, Insoon Jo, Jinyoung Lee, Jonghyun Yoon, Jeong-Uk Kang, Moonsang Kwon, Chanho Yoon, Sangyeun Cho et al. 2016. Biscuit: A framework for near-data processing of big data workloads. In ACM SIGARCH Comput. Archit. News, Vol. 44. IEEE Press, 153--165.Google ScholarGoogle Scholar
  29. Aayush Gupta, Youngjae Kim, and Bhuvan Urgaonkar. 2009. DFTL: A Flash Translation Layer Employing Demand-based Selective Caching of Page-level Address Mappings. Vol. 44. ACM.Google ScholarGoogle ScholarDigital LibraryDigital Library
  30. Trevor Hastie and Robert Tibshirani. 1996. Discriminant adaptive nearest neighbor classification and regression. In Proceedings of the International Conference on Advances in Neural Information Processing Systems. 409--415.Google ScholarGoogle Scholar
  31. David Held, Sebastian Thrun, and Silvio Savarese. 2016. Learning to track at 100 fps with deep regression networks. In Proceedings of the European Conference on Computer Vision. Springer, 749--765.Google ScholarGoogle ScholarCross RefCross Ref
  32. João F. Henriques, Rui Caseiro, Pedro Martins, and Jorge Batista. 2014. High-speed tracking with kernelized correlation filters. IEEE Trans. Pattern Anal. Mach. Intell. 37, 3 (2014), 583--596.Google ScholarGoogle ScholarDigital LibraryDigital Library
  33. Ali HeydariGorji, Siavash Rezaei, Mahdi Torabzadehkashi, Hossein Bobarshad, Vladimir Alves, and Pai H. Chou. 2020. HyperTune: Dynamic hyperparameter tuning for efficient distribution of DNN training over heterogeneous systems. Arxiv Preprint Arxiv:2007.08077 (2020).Google ScholarGoogle Scholar
  34. Ali HeydariGorji, Mahdi Torabzadehkashi, Siavash Rezaei, Hossein Bobarshad, Vladimir Alves, and Pai H. Chou. 2020. STANNIS: Low-Power Acceleration of Deep Neural Network Training Using Computational Storage Devices. Retrieved from arxiv:cs.DC/2002.07215.Google ScholarGoogle Scholar
  35. Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q . Weinberger. 2017. Densely connected convolutional networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 4700--4708.Google ScholarGoogle ScholarCross RefCross Ref
  36. Jeff Janukowicz. October 2018. How New QLC SSDs Will Change the Storage Landscape. Technical Report. Micron. Retrieved from https://www.micron.com/-/media/client/global/documents/products/white-paper/how_new_qlc_ssds_will_change_the_storage_landscape.pdf?la=en.Google ScholarGoogle Scholar
  37. Herve Jegou, Matthijs Douze, and Cordelia Schmid. 2011. Product quantization for nearest neighbor search. IEEE Trans. Pattern Anal. Mach. Intell. 33, 1 (2011), 117--128.Google ScholarGoogle ScholarDigital LibraryDigital Library
  38. Yangqing Jia, Evan Shelhamer, Jeff Donahue, Sergey Karayev, Jonathan Long, Ross Girshick, Sergio Guadarrama, and Trevor Darrell. 2014. Caffe: Convolutional architecture for fast feature embedding. Arxiv Preprint Arxiv:1408.5093 (2014).Google ScholarGoogle Scholar
  39. Insoon Jo, Duck-Ho Bae, Andre S. Yoon, Jeong-Uk Kang, Sangyeun Cho, Daniel D. G. Lee, and Jaeheon Jeong. 2016. YourSQL: A high-performance database system leveraging in-storage computing. Proc. VLDB Endow. 9, 12 (2016), 924--935.Google ScholarGoogle ScholarDigital LibraryDigital Library
  40. Sang-Woo Jun, Ming Liu, Sungjin Lee, Jamey Hicks, John Ankcorn, Myron King, Shuotao Xu, and Arvind. 2015. BlueDBM: An appliance for big data analytics. In Proceedings of the 42nd Annual International Symposium on Computer Architecture (ISCA ’15). ACM, New York, NY, 1--13. DOI:https://doi.org/10.1145/2749469.2750412Google ScholarGoogle ScholarDigital LibraryDigital Library
  41. Yangwook Kang, Yang-suk Kee, Ethan L. Miller, and Chanik Park. 2013. Enabling cost-effective data processing with smart SSD. In Proceedings of the IEEE 29th Symposium on Mass Storage Systems and Technologies (MSST’13). IEEE, 1--12.Google ScholarGoogle ScholarCross RefCross Ref
  42. Sungchan Kim, Hyunok Oh, Chanik Park, Sangyeun Cho, Sang-Won Lee, and Bongki Moon. 2016. In-storage processing of database scans and joins. Inf. Sci. 327 (2016), 183--200.Google ScholarGoogle ScholarDigital LibraryDigital Library
  43. Gunjae Koo, Kiran Kumar Matam, H. V. Narra, Jing Li, Hung-Wei Tseng, Steven Swanson, Murali Annavaram et al. 2017. Summarizer: Trading communication with computing near storage. In Proceedings of the 50th Annual IEEE/ACM International Symposium on Microarchitecture. ACM, 219--231.Google ScholarGoogle ScholarDigital LibraryDigital Library
  44. Matej Kristan, Jiri Matas, Aleš Leonardis, Tomas Vojir, Roman Pflugfelder, Gustavo Fernandez, Georg Nebehay, Fatih Porikli, and Luka Čehovin. 2016. A novel performance evaluation methodology for single-target trackers. IEEE Trans. Pattern Anal. Mach. Intell. 38, 11 (Nov. 2016), 2137--2155. DOI:https://doi.org/10.1109/TPAMI.2016.2516982Google ScholarGoogle ScholarDigital LibraryDigital Library
  45. Bo Li, Junjie Yan, Wei Wu, Zheng Zhu, and Xiaolin Hu. 2018. High performance visual tracking with siamese region proposal network. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 8971--8980.Google ScholarGoogle ScholarCross RefCross Ref
  46. Rafael Lima De Carvalho, Danilo S. C. Carvalho, Felix Mora-Camino, Priscila V. M. Lima, and Felipe M. G. França. 2014. Online tracking of multiple objects using WiSARD. In Proceedings of the 22nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. 541--546. Retrieved from https://hal-enac.archives-ouvertes.fr/hal-01059678.Google ScholarGoogle Scholar
  47. David G. Lowe. 2004. Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60, 2 (2004), 91--110.Google ScholarGoogle ScholarDigital LibraryDigital Library
  48. David J. C. MacKay and Radford M. Neal. 1996. Near Shannon limit performance of low density parity check codes. Electron. Lett. 32, 18 (1996), 1645--1646.Google ScholarGoogle ScholarCross RefCross Ref
  49. Pankaj Mehra. 2019. Samsung smartSSD: Accelerating data-rich applications. In Proceedings of the Flash Memory Summit.Google ScholarGoogle Scholar
  50. Dirk Merkel. 2014. Docker: Lightweight Linux containers for consistent development and deployment. Linux J. 2014, 239 (2014), 2.Google ScholarGoogle ScholarDigital LibraryDigital Library
  51. Hyeonseob Nam and Bohyung Han. 2016. Learning multi-domain convolutional neural networks for visual tracking. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 4293--4302.Google ScholarGoogle ScholarCross RefCross Ref
  52. Iyswarya Narayanan, Di Wang, Myeongjae Jeon, Bikash Sharma, Laura Caulfield, Anand Sivasubramaniam, Ben Cutler, Jie Liu, Badriddine Khessib, and Kushagra Vaid. 2016. SSD failures in datacenters: What? when? and why?. In Proceedings of the 9th ACM International on Systems and Storage Conference. ACM.Google ScholarGoogle ScholarDigital LibraryDigital Library
  53. Rishiyur S. Nikhil. 2009. What is bluespec? ACM SIGDA Newslett. 39, 1 (2009), 1--1.Google ScholarGoogle ScholarDigital LibraryDigital Library
  54. Guanghan Ning, Zhi Zhang, Chen Huang, Xiaobo Ren, Haohong Wang, Canhui Cai, and Zhihai He. 2017. Spatially supervised recurrent convolutional neural networks for visual object tracking. In Proceedings of the IEEE International Symposium on Circuits and Systems (ISCAS’17). IEEE, 1--4.Google ScholarGoogle ScholarCross RefCross Ref
  55. Shigeo Ohshima and Yoichiro Tanaka. 2016. New 3D flash technologies offer both low cost and low power solutions. In Proceedings of the Flash Memory Summit.Google ScholarGoogle Scholar
  56. ONFI online. 2017. Open NAND Flash interface specification. Retrieved from http://www.onfi.org/specifications.Google ScholarGoogle Scholar
  57. PCI-SIG. 2020. Retrieved from https://pcisig.com/specifications/pciexpress/M.2_Specification/.Google ScholarGoogle Scholar
  58. Joseph Redmon, Santosh Divvala, Ross Girshick, and Ali Farhadi. 2016. You only look once: Unified, real-time object detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 779--788.Google ScholarGoogle ScholarCross RefCross Ref
  59. Joseph Redmon and Ali Farhadi. 2018. Yolov3: An incremental improvement. Arxiv Preprint Arxiv:1804.02767 (2018).Google ScholarGoogle Scholar
  60. IDC Report. 2018. The digitization of the world from edge to core. Retrieved from https://www.seagate.com/files/www-content/our-story/trends/files/idc-seagate-dataage-whitepaper.pdf.Google ScholarGoogle Scholar
  61. Siavash Rezaei, Eli Bozorgzadeh, and Kanghee Kim. 2019. UltraShare: FPGA-based dynamic accelerator sharing and allocation. In Proceedings of the International Conference on ReConFigurable Computing and FPGAs (ReConFig’19). IEEE, 1--5.Google ScholarGoogle ScholarCross RefCross Ref
  62. S. Rezaei, C. Hernandez-Calderon, S. Mirzamohammadi, E. Bozorgzadeh, A. Veidenbaum, A. Nicolau, and M. J. Prather. 2016. Data-rate-aware FPGA-based acceleration framework for streaming applications. In Proceedings of the International Conference on ReConFigurable Computing and FPGAs (ReConFig’16). 1--6. DOI:https://doi.org/10.1109/ReConFig.2016.7857162Google ScholarGoogle Scholar
  63. Siavash Rezaei, Kanghee Kim, and Eli Bozorgzadeh. 2018. Scalable multi-queue data transfer scheme for FPGA-based multi-accelerators. In Proceedings of the IEEE 36th International Conference on Computer Design (ICCD’18). 374--380.Google ScholarGoogle ScholarCross RefCross Ref
  64. Steve Roddy. 2019. Arm NN: the Easy Way to Deploy Edge ML. Retrieved from https://community.arm.com/developer/tools-software/tools/b/tools-software-ides-blog/posts/arm-nn-the-easy-way-to-deploy-edge-ml?_ga=2.9822706.6940669.1579038789-277442185.1570226249.Google ScholarGoogle Scholar
  65. Mohammad Samragh, Mojan Javaheripi, and Farinaz Koushanfar. 2019. CodeX: Bit-flexible encoding for streaming-based FPGA acceleration of DNNs. CoRR abs/1901.05582 (2019).Google ScholarGoogle Scholar
  66. Scaleflux. 2020. Retrieved from http://scaleflux.com/index.html.Google ScholarGoogle Scholar
  67. Sudharsan Seshadri, Mark Gahagan, Meenakshi Sundaram Bhaskaran, Trevor Bunker, Arup De, Yanqin Jin, Yang Liu, and Steven Swanson. 2014. Willow: A user-programmable SSD. In Proceedings of the Symposium on Operating Systems Design and Implementation. 67--80.Google ScholarGoogle Scholar
  68. Konstantin Shvachko, Hairong Kuang, Sanjay Radia, and Robert Chansler. 2010. The Hadoop distributed file system. In Proceedings of the IEEE 26th Symposium on Mass Storage Systems and Technologies (MSST’10). IEEE, 1--10.Google ScholarGoogle ScholarDigital LibraryDigital Library
  69. Josef Sivic and Andrew Zisserman. 2003. Video Google: A text retrieval approach to object matching in videos. In Proceedings of the International Conference on Computer Vision, Vol. 2. IEEE, 1470–1477.Google ScholarGoogle ScholarCross RefCross Ref
  70. SNIA. 2019. Computational Storage Technical Working Group. Retrieved from https://www.snia.org/computational.Google ScholarGoogle Scholar
  71. SolarWinds. 2018. Can gzip Compression Really Improve Web Performance? Retrieved from https://royal.pingdom.com/can-gzip-compression-really-improve-web-performance/.Google ScholarGoogle Scholar
  72. Steven R. Soltis, G. M. Erickson, Kenneth W. Preslan, Matthew T. O’Keefe, and Thomas M. Ruwart. 1997. The global file system: A file system for shared disk storage. IEEE Transactions on Parallel and Distributed Systems 1 (1997), 1.Google ScholarGoogle Scholar
  73. Mohan Srinivasan. 2014. Flashcache. Retrieved from https://github.com/facebookarchive/flashcache.Google ScholarGoogle Scholar
  74. NGD Systems. 2020. Retrieved from https://www.ngdsystems.com.Google ScholarGoogle Scholar
  75. Ted Friedman, Thomas Bittman, Neil MacDonald. 2019. How to Overcome Four Major Challenges in Edge Computing. Retrieved from https://www.gartner.com/doc/reprints?id=1-1XWDQ2PW8ct=1912108st=sb.Google ScholarGoogle Scholar
  76. Devesh Tiwari, Simona Boboila, Sudharshan S. Vazhkudai, Youngjae Kim, Xiaosong Ma, Peter Desnoyers, and Yan Solihin. 2013. Active flash: Towards energy-efficient, in-situ data analytics on extreme-scale machines. In Proceedings of the USENIX Conference on File and Storage Technologies. 119--132.Google ScholarGoogle Scholar
  77. Mahdi Torabzadehkashi, Ali Heydarigorji, Siavash Rezaei, Hosein Bobarshad, Vladimir Alves, and Nader Bagherzadeh. 2019. Accelerating HPC applications using computational storage devices. In Proceedings of the IEEE 21st International Conference on High Performance Computing and Communications; IEEE 17th International Conference on Smart City; IEEE 5th International Conference on Data Science and Systems (HPCC/SmartCity/DSS’19). IEEE, 1878--1885.Google ScholarGoogle Scholar
  78. Mahdi Torabzadehkashi, Siavash Rezaei, Vladimir Alves, and Nader Bagherzadeh. 2018. CompStor: An in-storage computation platform for scalable distributed processing. In Proceedings of the IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW’18). IEEE, 1260--1267.Google ScholarGoogle ScholarCross RefCross Ref
  79. Mahdi Torabzadehkashi, Siavash Rezaei, Ali Heydarigorji, Hosein Bobarshad, Vladimir Alves, and Nader Bagherzadeh. 2019. Catalina: In-storage processing acceleration for scalable big data analytics. In Proceedings of the 27th Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP’19). IEEE, 430--437.Google ScholarGoogle ScholarCross RefCross Ref
  80. Mahdi Torabzadehkashi, Siavash Rezaei, Ali HeydariGorji, Hosein Bobarshad, Vladimir Alves, and Nader Bagherzadeh. 2019. Computational storage: An efficient and scalable platform for big data and HPC applications. J. Big Data 6, 1 (2019), 100.Google ScholarGoogle ScholarCross RefCross Ref
  81. Jack Valmadre, Luca Bertinetto, João Henriques, Andrea Vedaldi, and Philip H. S. Torr. 2017. End-to-end representation learning for correlation filter based tracking. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2805--2813.Google ScholarGoogle Scholar
  82. DRAM Exchange Website. 2020. DRAM Exchange. Retrieved from https://www.dramexchange.com.Google ScholarGoogle Scholar
  83. Part Stock Website. 2020. Part Stock Specs for Xilinx XCZU19EG-2FFVC1760E. Retrieved from http://www.part-stock.com/product-part/xilinx__XCZU19EG-2FFVC1760E.html.Google ScholarGoogle Scholar
  84. Kilian Q. Weinberger and Lawrence K. Saul. 2009. Distance metric learning for large margin nearest neighbor classification. J. Mach. Learn. Res. 10(Feb. 2009), 207--244.Google ScholarGoogle Scholar
  85. Yi Wu, Jongwoo Lim, and Ming-Hsuan Yang. 2015. Object tracking benchmark. IEEE Trans. Pattern Anal. Mach. Intell. 37, 9 (2015), 1834--1848.Google ScholarGoogle ScholarDigital LibraryDigital Library
  86. Ming-Chang Yang, Yu-Ming Chang, Che-Wei Tsao, Po-Chun Huang, Yuan-Hao Chang, and Tei-Wei Kuo. 2014. Garbage collection and wear leveling for flash memory: Past and future. In Proceedings of the International Conference on Smart Computing. IEEE, 66--73.Google ScholarGoogle ScholarCross RefCross Ref
  87. Kai Zhao, Wenzhe Zhao, Hongbin Sun, Xiaodong Zhang, Nanning Zheng, and Tong Zhang. 2013. LDPC-in-SSD: Making advanced error correction codes work effectively in solid state drives. In Proceedings of the 11th USENIX Conference on File and Storage Technologies (FAST’13). 243--256.Google ScholarGoogle ScholarDigital LibraryDigital Library

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