CNN Inference acceleration using low-power devices for human monitoring and security scenarios. (December 2020)

• Record Type: Journal Article
• Authors: Mas, Juan; Panadero, Teodoro; Botella, Guillermo; Del Barrio, Alberto A.; García, Carlos
• Is Part Of: Computers & electrical engineering. Volume 88(2020)
• Publication Date: 2020-12
• Subjects: Convolutional neural network -- OpenVINO -- Neural compute stick 2 -- Cluster -- Inference; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2020.106859
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: 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 multi-face 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.