• DocumentCode
    3323488
  • Title

    An energy-efficient heterogeneous dual-core processor for Internet of Things

  • Author

    Zhibo Wang ; Yongpan Liu ; Yinan Sun ; Yang Li ; Daming Zhang ; Huazhong Yang

  • Author_Institution
    Dept. of EE, Tsinghua Univ., Beijing, China
  • fYear
    2015
  • fDate
    24-27 May 2015
  • Firstpage
    2301
  • Lastpage
    2304
  • Abstract
    With the fast development of Internet of Things (IoTs) in recent years, many IoT applications, such as structure health monitoring, surveillance camera and etc, require both extensive computation for burst-mode signal processing as well as ultra low power continuous operations. However, most of conventional IoT processors focus on ultra low power consumption and cannot satisfy those demands. This paper proposes a novel energy-efficient heterogenous dual-core processor, which includes both an ultra low power near-threshold CoreL and a fast CoreH to meet those emerging requirements. Furthermore, an optimal framework is proposed to realize energy efficient task mapping and scheduling. The processor is fabricated and its energy consumption in low power mode is as low as 7.7pJ/cycle and outperforms related work. Detailed analysis under several real applications shows that up to 2.62× energy efficiency improvements can be achieved without deadline miss compared with the high-performance-only signle core architecture.
  • Keywords
    Internet of Things; coprocessors; power aware computing; scheduling; CoreH; Internet of Things; IoT; burst-mode signal processing; energy consumption; energy efficient task mapping; energy efficient task scheduling; energy-efficient heterogeneous dual-core processor; ultralow power near-threshold CoreL; Benchmark testing; Computer architecture; Energy consumption; Monitoring; Processor scheduling; Random access memory; Semiconductor device measurement;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Circuits and Systems (ISCAS), 2015 IEEE International Symposium on
  • Conference_Location
    Lisbon
  • Type

    conf

  • DOI
    10.1109/ISCAS.2015.7169143
  • Filename
    7169143