• DocumentCode
    1358600
  • Title

    Tera-Scale Performance Machine Learning SoC (MLSoC) With Dual Stream Processor Architecture for Multimedia Content Analysis

  • Author

    Chen, Tse-Wei ; Tang, Chi-Sun ; Tsai, Sung-Fang ; Tsai, Chen-Han ; Chien, Shao-Yi ; Chen, Liang-Gee

  • Author_Institution
    Dept. of Electr. Eng., Nat. Taiwan Univ., Taipei, Taiwan
  • Volume
    45
  • Issue
    11
  • fYear
    2010
  • Firstpage
    2321
  • Lastpage
    2329
  • Abstract
    A new machine learning SoC (MLSoC) for multimedia content analysis is implemented with 16-mm2 area in 90-nm CMOS technology. Different from traditional VLSI architectures, it focuses on the coacceleration of computer vision and machine learning algorithms, and two stream processors with massively parallel processing elements are integrated to achieve tera-scale performance. In the dual stream processor (DSP) architecture, the data are transferred between processors and the high-bandwidth dual memory (HBDM) through the local media bus without consuming the AMBA AHB bandwidth. The image stream processor (ISP) of the MLSoC can handle common window-based operations for image processing, and the feature stream processor (FSP) can deal with machine learning algorithms with different dimensions. The power efficiency of the proposed MLSoC is 1.7 TOPS/W, and the area efficiency is 81.3 GOPS/mm 2.
  • Keywords
    computer vision; digital circuits; learning (artificial intelligence); multimedia systems; system-on-chip; CMOS technology; computer vision; dual stream processor architecture; feature stream processor; image stream processor; multimedia content analysis; tera-scale performance machine learning SoC; Bandwidth; Computer architecture; Digital signal processing; Machine learning algorithms; Multimedia communication; Pixel; Streaming media; Digital circuit; hardware architecture; machine learning; multimedia content analysis; system-on-a-Chip (SoC);
  • fLanguage
    English
  • Journal_Title
    Solid-State Circuits, IEEE Journal of
  • Publisher
    ieee
  • ISSN
    0018-9200
  • Type

    jour

  • DOI
    10.1109/JSSC.2010.2067910
  • Filename
    5607247