An Auto-tuning Assisted Power-Aware Study of Iris Matching Algorithm on Intel's SCC

作者：Torres Gildo^*; Liu Chen; Chang Jed Kao Tung; Hua Fang; Schuckers Stephanie

来源：Journal of Signal Processing Systems for Signal Image and Video Technology, 2015, 80(3): 261-276.

DOI：10.1007/s11265-014-0901-4

• Single-chip cloud computer
• Iris pattern recognition
• Biometrics
• Dynamic voltage and frequency scaling
• Power-aware computing
• Many-core processor

Biometric applications become paramount across private sectors, industry, as well as government agencies. As large amount of data being collected from many different sources, managing such volumes of data and developing efficient and effective large-scale operational solutions have become a concern. For example, real-time identification of individuals with the purpose of allowing or denying them access to specific systems or resource is challenging from the performance point of view. In addition, processing large amounts of data requires a significant amount of energy. The Single-chip Cloud Computer (SCC) is an experimental processor created by Intel Labs. In this paper we employ SCC, which supports different configurations in terms of number of cores, frequency, and voltage settings, to investigate the power-aware computing and performance enhancement of an iris matching algorithm on such many-core architecture. This biometric application contains a large degree of parallelism that can be exploited by porting it onto the SCC. Various metrics such as performance, power, energy, energy delay product (EDP), and power per speedup (PPS) are studied when executing the application under different SCC configurations. We also analyze how the results of these metrics vary as we change different parameters. In the latest stage of this study, we apply an auto-tuning approach based on Differential Evolution (DE) algorithm in an effort to quickly approaching the optimal configuration of the SCC based on the targeted metric. This allows us to traverse only a portion of the search space. Such approach proves to be very useful for energy-related metrics.

• 出版日期2015-9