Human emotion based real-time memory and computation management on resource-limited edge devices

Abstract

Emotional AI or Affective Computing has been projected to grow rapidly in the upcoming years. Despite many existing developments in the application space, there has been a lack of hardware-level exploitation of the user's emotions. In this paper, we propose a deep collaboration between user's affects and the hardware system management on resource-limited edge devices. Based on classification results from efficient affect classifiers on smartphone devices, novel real-time management schemes for memory, and video processing are proposed to improve the energy efficiency of mobile devices. Case studies on H.264 / AVC video playback and Android smartphone usages are provided showing significant power saving of up to 23% and reduction of memory loading of up to 17% using the proposed affect adaptive architecture and system management schemes.