期刊
COMPUTERS & ELECTRICAL ENGINEERING
卷 88, 期 -, 页码 -出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.compeleceng.2020.106859
关键词
Convolutional neural network; OpenVINO; Neural compute stick 2; Cluster; Inference
类别
资金
- EU (FEDER)
- Spanish MINECO [52018/TCS-4423, RTI2018093684-B-I00, TIN 2015-65277-R]
- CM [52018/TCS-4423, RTI2018093684-B-I00, TIN 2015-65277-R]
- UCM-Banco Santander Grant [PR26-16/20B-1]
- UCM - Innova docentia competitive Project: 2018-2019 [315]
- UCM - Innova docentia competitive Project: 2019-2020 [205]
Security is currently one of the top concerns in our society. From governmental installations to private companies and medical institutions, they all have to address directly with security issues as: access to restricted information quarantine control, or criminal tracking. As an example, identifying patients is critical in hospitals or geriatrics in order to isolate infected people, which has proven to be a non- trivial issue with the COVID-19 pandemic that is currently affecting all countries, or to locate fled patients. Face recognition is then a non-intrusive alternative for performing these tasks. Although FaceNet from Google has proved to be almost perfect, in a multiface scenario its performance decays rapidly. In order to mitigate this loss of performance, in this paper a cluster based on the Neural Computer Stick version 2 and OpenVINO by Intel is proposed. A detailed power and runtime study is shown for two programming models, namely: multithreading and multiprocessing. Furthermore, 3 different hosts have been considered. In the most efficient configuration, an average of 6 frames per second has been achieved using the Raspberry Pi 4 as host and with a power consumption of just 11.2W, increasing by a factor of 3.3X the energy efficiency with respect to a PC-based solution in a multi-face scenario.
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