Skip navigation

DSpace

機構典藏 DSpace 系統致力於保存各式數位資料(如:文字、圖片、PDF)並使其易於取用。

點此認識 DSpace
DSpace logo
English
中文
  • 瀏覽論文
    • 校院系所
    • 出版年
    • 作者
    • 標題
    • 關鍵字
    • 指導教授
  • 搜尋 TDR
  • 授權 Q&A
    • 我的頁面
    • 接受 E-mail 通知
    • 編輯個人資料
  1. NTU Theses and Dissertations Repository
  2. 電機資訊學院
  3. 資訊工程學系
請用此 Handle URI 來引用此文件: http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/68780
完整後設資料紀錄
DC 欄位值語言
dc.contributor.advisor楊佳玲
dc.contributor.authorChien-I Leeen
dc.contributor.author李建誼zh_TW
dc.date.accessioned2021-06-17T02:35:00Z-
dc.date.available2019-08-25
dc.date.copyright2017-08-25
dc.date.issued2017
dc.date.submitted2017-08-17
dc.identifier.citation[1] ”ARM Energy Probe”. https://developer.arm.com/products/software-development-tools/ds-5-development-studio/streamline/arm-energy-probe”.
[2] ”ARM Streamline Analyzer”. ”https://developer.arm.com/products/software-development-tools/ds-5-development-studio/streamline”.
[3] ”autobench 1.0”. ”http://www.eembc.org/benchmark/automotive sl.php”.
[4] ”Caffe”. ”http://caffe.berkeleyvision.org/”.
[5] ”CHiME Challenge”. ”http://spandh.dcs.shef.ac.uk/chime challenge/data.html”.
[6] ”HTK”. ”http://htk.eng.cam.ac.uk/”.
[7] ”iotmark”. ”http://www.eembc.org/iot-connect/about.php”.
[8] ”LIBSVM-suggestion for choosing kernel function”. ”http://www.csie.ntu.edu.tw/ cjlin/libsvm/faq.html#f506”.
[9] ”MRPT”. ”http://www.mrpt.org/”.
[10] ”OpenBLAS”. ”http://www.openblas.net/”.
[11] ”Raspberry Pi”. ”https://www.raspberrypi.org/products/raspberry-pi-3-model-b/”.
[12] ”RPi power report”. https://www.raspberrypi.org/help/faqs/#power.
[13] ”Sound examples from Dan Ellis”. ”http://www.ee.columbia.edu/ dpwe/sounds/”.
[14] ”Valgrind”. ”http://valgrind.org/”.
[15] ”Video surveillence frame rate comparison”. ”http://www.cctvcamerapros.com/CCTV-Video-Frame-Rate-Comparison-s/739.htm”.
[16] X. Anguera, C. Wooters, and J. Hernando. Acoustic beamforming for speaker diarization of meetings. IEEE TASLP, 2007.
[17] M. F. Arlitt, M. Marwah, G. Bellala, A. Shah, J. Healey, and B. Vandiver. Iotabench: an internet of things analytics benchmark. 2015.
[18] J.-L. Blanco, F.-A. Moreno, and J. Gonzalez-Jimenez. The mlaga urban dataset: High-rate stereo and lidars in a realistic urban scenario. International Journal of Robotics Research.
[19] F. Bonomi, R. Milito, J. Zhu, and S. Addepalli. Fog computing and its role in the internet of things. In Proceedings of the first edition of the MCC workshop on Mobile cloud computing.
[20] W. K. Chan, J. Y. Chang, T. W. Chen, Y. H. Tseng, and S. Y. Chien. Efficient content analysis engine for visual surveillance network. IEEE Transactions on Circuits and Systems for Video Technology, 2009.
[21] C.-C. Chang and C.-J. Lin. Libsvm: A library for support vector machines. 2011.
[22] D. W. Chang, C. D. Jenkins, P. C. Garcia, S. Z. Gilani, P. Aguilera, A. Nagarajan, M. J. Anderson, M. A. Kenny, S. M. Bauer, M. J. Schulte, and K. Compton. Ercbench: An open-source benchmark suite for embedded and reconfigurable computing. In 2010 International Conference on Field Programmable Logic and Applications, 2010.
[23] G. Chen, H. Yang, and L. Huang. Optimizing Compressive Sensing in the Internet of Things.
[24] Y. K. Chen. Challenges and opportunities of internet of things. In 2012, ASP-DAC.
[25] C. EMMANOUIL. COMPARISON OF OPEN SOURCE STEREO VISION ALGORITHMS. PhD thesis, 2015.
[26] D. Evans. The internet of things: How the next evolution of the internet is changing every-thing. CISCO white paper, 2011.
[27] Y. Fu, Y. Guo, Y. Zhu, F. Liu, C. Song, and Z. H. Zhou. Multi-view video summarization. IEEE Transactions on Multimedia, 2010.
[28] J. Gubbi, R. Buyya, S. Marusic, and M. Palaniswami. Internet of things (iot): A vision, architectural elements, and future directions. CoRR.
[29] M. R. Guthaus, J. S. Ringenberg, D. Ernst, T. M. Austin, T. Mudge, and R. B. Brown. Mibench: A free, commercially representative embedded benchmark suite. In Proceedings of the Fourth Annual IEEE International Workshop on Workload Characterization. WWC-4 (Cat. No.01EX538), 2001.
[30] S. Han, X. Liu, H. Mao, J. Pu, A. Pedram, M. A. Horowitz, and W. J. Dally. Eie: efficient inference engine on compressed deep neural network. 2016.
[31] H. Hirschmuller and D. Scharstein. Evaluation of cost functions for stereo matching. In 2007 CVPR, 2007.
[32] F. N. Iandola, M. W. Moskewicz, K. Ashraf, S. Han, W. J. Dally, and K. Keutzer. Squeezenet: Alexnet-level accuracy with 50x fewer parameters and <1mb model size. arXiv:1602.07360, 2016.
[33] H. T. Kung. Big Data and Compressive Sensing.
[34] Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 1998.
[35] H.-T. Lin and C.-J. Lin. A study on sigmoid kernels for svm and the training of non-psd kernels by smo-type methods.
[36] Y.-M. Lin, J.-F. Zhang, J. Geng, and A.-Y. A. Wu. Structural scrambling of circulant matrices for cost-effective compressive sensing.
[37] I. S. MacKenzie and L. Chang. A performance comparison of two handwriting recognizers. Interacting with Computers, 1999.
[38] K. Madsen, H. B. Nielsen, and O. Tingleff. Methods for non-linear least squares problems. 1999.
[39] M. Mehrabani, S. Bangalore, and B. Stern. Personalized speech recognition for internet of things. In 2015 IEEE 2nd World Forum on Internet of Things (WF-IoT), 2015.
[40] G. B. Moody and R. G. Mark. The impact of the mit-bih arrhythmia database. IEEE Engi-neering in Medicine and Biology Magazine, 2001.
[41] S. H. Ou, C. H. Lee, V. S. Somayazulu, Y. K. Chen, and S. Y. Chien. On-line multi-view video summarization for wireless video sensor network. IEEE Journal of Selected Topics in Signal Processing, 2015.
[42] D. Pandiyan and C. J. Wu. Quantifying the energy cost of data movement for emerging smart phone workloads on mobile platforms. In 2014, IISWC.
[43] J. C. Platt. Advances in kernel methods. chapter Fast Training of Support Vector Machines Using Sequential Minimal Optimization.
[44] W. Shi, J. Cao, Q. Zhang, Y. Li, and L. Xu. Edge computing: Vision and challenges. 2016,IEEE Internet of Things Journal.
[45] A. Shukla, S. Chaturvedi, and Y. Simmhan. Riotbench: A real-time iot benchmark for distributed stream processing platforms. CoRR, 2017.
[46] C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich. Going deeper with convolutions. In CVPR, 2015.
[47] M. Valera and S. A. Velastin. Intelligent distributed surveillance systems: a review. IEEE Proceedings - Vision, Image and Signal Processing, 2005
dc.identifier.urihttp://tdr.lib.ntu.edu.tw/jspui/handle/123456789/68780-
dc.description.abstract物聯網(Internet of Things, IoT) 是科技應用上的一種新形態。它利用廣佈於環境中的智慧型裝置收集資料並在裝置間互動處理。為了能夠處理大量資料並聰明地提供資訊與建議,這些物聯網裝置必須俱備相當的運算能力與高效率的通訊策略。然而,由於缺乏俱有合格的基準程式來代表物聯網中多元的應用,物聯網裝置上相關的效能研究相當貧乏。
這篇論文提出了IoTBench,是第一個針對物聯網裝置應用所設計的基準程式
集。本程式集中包含了四個主要分類與八支具有代表性的程式。我們在論文中檢視了IoTBench在現今熱門的物聯網硬體平台上的計算需求與執行效率。我們也藉由分析方法來找出IoTBench在能量消耗上的特性。整體來說,IoTBench替未來新式的硬體架構設計與物聯網系統開發建立了基礎。我們也期待本文中的特性分析可以對整體物聯網發展帶來更多啟發。
zh_TW
dc.description.abstractInternet of Things (IoT) is the new paradigm where the widespread intelligent devices collect data from the environment and automatically interact with others. To process a great amount of data and provide human with information and suggestion, IoT devices must have a scalable computational ability and an efficient communication strategy. However, due to the lack of benchmarks representative to the diverse IoT applications, there are limited performance studies on IoT devices.
This thesis presents IoTBench, the first benchmark suite targeting at IoT edge-device applications. This suite includes eight most representative programs from four major IoT categories. We investigate the computational demand and execution efficiency of these benchmarks running on a popular IoT device platform. We also analyze and characterize the energy consumption of IoTBench using analytic approaches. Overall, IoTBench establishes a foundation for innovative architecture design and system solution of IoT applications. We also expect the characterization study and insights can inspire future
development of IoT edge devices.
en
dc.description.provenanceMade available in DSpace on 2021-06-17T02:35:00Z (GMT). No. of bitstreams: 1
ntu-106-R04922085-1.pdf: 1597675 bytes, checksum: e222091a9f8a48ef1612d23702c61a21 (MD5)
Previous issue date: 2017
en
dc.description.tableofcontentsAbstract iii
List of Figures vi
List of Tables vii
Chapter 1 Introduction 1
Chapter 2 Overview of IoTBench 4
2.1 Vision . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
2.2 Physiological Signal Processing . . . . . . . . . . . . . . . . . . . . . . 7
2.3 Analytics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
2.4 Speech Recognition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
Chapter 3 Evaluated Methodology 12
3.1
Experiment platform . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
3.2 Performance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
3.3 Energy / Power . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
Chapter 4 Characterization 17
4.1 Computational Demand . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
4.2 Execution Efficiency . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
4.3 Power and Energy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
Chapter 5 Related Works 29
Chapter 6 Conclusion 30
Bibliography 31
Chapter A Micro-benchmark Codes A.1
Chapter B Maximal Power Estimation B.8
dc.language.isoen
dc.subject基準程式zh_TW
dc.subject耗能分析zh_TW
dc.subject物聯網zh_TW
dc.subject計算效率分析zh_TW
dc.subject邊緣計算zh_TW
dc.subjectcharacterizationen
dc.subjectInternet of Thingsen
dc.subjectBenchmark suiteen
dc.subjectEdge computingen
dc.titleIoTBench: 物聯網基準程式集zh_TW
dc.titleIoTBench: An benchmark suite for Internet of Thingsen
dc.typeThesis
dc.date.schoolyear105-2
dc.description.degree碩士
dc.contributor.oralexamcommittee呂仁碩,鄭湘筠,張原豪,王克中
dc.subject.keyword物聯網,基準程式,邊緣計算,計算效率分析,耗能分析,zh_TW
dc.subject.keywordInternet of Things,Benchmark suite,Edge computing,characterization,en
dc.relation.page33
dc.identifier.doi10.6342/NTU201703488
dc.rights.note有償授權
dc.date.accepted2017-08-17
dc.contributor.author-college電機資訊學院zh_TW
dc.contributor.author-dept資訊工程學研究所zh_TW
顯示於系所單位:資訊工程學系

文件中的檔案:
檔案 大小格式 
ntu-106-1.pdf
  未授權公開取用
1.56 MBAdobe PDF
顯示文件簡單紀錄


系統中的文件,除了特別指名其著作權條款之外,均受到著作權保護,並且保留所有的權利。

社群連結
聯絡資訊
10617臺北市大安區羅斯福路四段1號
No.1 Sec.4, Roosevelt Rd., Taipei, Taiwan, R.O.C. 106
Tel: (02)33662353
Email: ntuetds@ntu.edu.tw
意見箱
相關連結
館藏目錄
國內圖書館整合查詢 MetaCat
臺大學術典藏 NTU Scholars
臺大圖書館數位典藏館
本站聲明
© NTU Library All Rights Reserved