The paper focuses on real-time facial expression recognition (FER) systems as an important component in various real-world applications such as social robotics. We investigate two hardware options for the deployment of FER machine learning (ML) models at the edge: neuromorphic hardware versus edge AI accelerators. Our study includes exhaustive experiments providing comparative analyses between the Intel Loihi neuromorphic processor and four distinct edge platforms: Raspberry Pi-4, Intel Neural Compute Stick (NSC), Jetson Nano, and Coral TPU. The results obtained show that Loihi can achieve approximately two orders of magnitude reduction in power dissipation and one order of magnitude energy savings compared to Coral TPU which happens to be the least power-intensive and energy-consuming edge AI accelerator. These reductions in power and energy are achieved while the neuromorphic solution maintains a comparable level of accuracy with the edge accelerators, all within the real-time latency requirements.

翻译：本文聚焦于实时面部表情识别系统，作为社交机器人等实际应用中的重要组成部分。我们研究了两种用于边缘部署面部表情识别机器学习模型的硬件方案：神经形态硬件与边缘AI加速器。本研究通过大量实验，对英特尔Loihi神经形态处理器与四种不同的边缘平台（Raspberry Pi-4、英特尔神经计算棒、Jetson Nano和Coral TPU）进行了对比分析。结果表明，与功耗和能量消耗最低的边缘AI加速器Coral TPU相比，Loihi可降低约两个数量级的功耗和一个数量级的能耗。在实现这些功耗与能效优势的同时，神经形态方案在实时延迟要求内，保持了与边缘加速器相当的准确率。