دانلود مقاله انگلیسی رایگان:شتاب دهنده سخت افزاری بهینه سازی شده برای برنامه های استخراج داده بر روی چهارچوب های embedded: مطالعه موردی تجزیه و تحلیل مؤلفه اصلی - 2019
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دانلود مقاله انگلیسی داده کاوی رایگان
  • Optimized hardware accelerators for data mining applications on embedded platforms: Case study principal component analysis Optimized hardware accelerators for data mining applications on embedded platforms: Case study principal component analysis
    Optimized hardware accelerators for data mining applications on embedded platforms: Case study principal component analysis

    سال انتشار:

    2019


    عنوان انگلیسی مقاله:

    Optimized hardware accelerators for data mining applications on embedded platforms: Case study principal component analysis


    ترجمه فارسی عنوان مقاله:

    شتاب دهنده سخت افزاری بهینه سازی شده برای برنامه های استخراج داده بر روی چهارچوب های embedded: مطالعه موردی تجزیه و تحلیل مؤلفه اصلی


    منبع:

    Sciencedirect - Elsevier - Microprocessors and Microsystems, 65 (2019) 79-96: doi:10:1016/j:micpro:2019:01:001


    نویسنده:

    S. Navid Shahrouzi, DarshikaG. Perera


    چکیده انگلیسی:

    With the proliferation of mobile, handheld, and embedded devices, many applications such as data min- ing applications have found their way into these devices. However, mobile devices have stringent area and power limitations, high speed-performance, reduced cost, and time-to-market requirements. Furthermore, applications running on mobile devices are becoming more complex requiring high processing power. These design constraints pose serious challenges to the embedded system designers. In order to pro- cess the applications on mobile and embedded systems, effectively and efficiently, optimized hardware architectures are needed. We are investigating the utilization of FPGA-based customized hardware to ac- celerate embedded data mining applications including handwritten analysis and facial recognition. For these biometric applications, Principal Component Analysis (PCA) is applied initially, followed by similar- ity measure. In this research work, we introduce novel and efficient embedded hardware architectures to accelerate the PCA computation. PCA is a classic technique to reduce the dimensionality of data by transforming the original data set into a new set of variables called Principal Components (PCs) that rep- resent the key features of the data. We propose two hardware versions for PCA computation, each with its unique optimization techniques to enhance the performance of our designs, and one specifically with additional techniques to reduce the memory access latency of embedded platforms. To the best of our knowledge, we could not find similar work for PCA, specifically catered to the embedded devices, in the published literature. We perform experiments to evaluate the feasibility and efficiency of our designs us- ing a benchmark dataset for biometrics. Our embedded hardware designs are generic, parameterized, and scalable; and achieve 78 times speedup as compared to its software counterparts
    Keywords: Data mining | Dimensionality reduction techniques | Embedded and mobile systems | FPGAs | Hardware acceleration | Principal Component Analysis


    سطح: متوسط
    تعداد صفحات فایل pdf انگلیسی: 18
    حجم فایل: 1996 کیلوبایت

    قیمت: رایگان


    توضیحات اضافی:




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