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.

本研究聚焦于实时人脸表情识别（FER）系统作为社会机器人等各种现实世界应用的重要组成部分。我们调查了两种用于边缘部署 FER 机器学习（ML）模型的硬件选择：神经形态硬件和边缘 AI 加速器。我们的研究包括详尽的实验，对比分析了 Intel Loihi 神经形态处理器与四种不同的边缘平台：Raspberry Pi-4、Intel Neural Compute Stick（NSC）、Jetson Nano 和 Coral TPU。实验结果表明，Loihi 与边缘加速器相比，能够实现功耗降低约两个数量级，以及能耗节约约一个数量级。这些功耗和能耗的降低是在维持神经形态解决方案与边缘加速器相当准确性的情况下实现的，而且都在实时延迟要求内。

实时面部表情识别：神经形态硬件对边缘人工智能加速器的比较

Realtime Facial Expression Recognition: Neuromorphic Hardware vs. Edge  AI Accelerators

Facial Expression Recognition (FER) plays an important role in human-computer
interactions and is used in a wide range of applications. Convolutional Neural
Networks (CNN) have shown promise in their ability to classify human facial
expressions, however, large CNNs are not well-suited to be implemented on
resource- and energy-constrained IoT devices. In this work, we present a
hierarchical framework for developing and optimizing hardware-aware CNNs tuned
for deployment at the edge. We perform a comprehensive analysis across various
edge AI accelerators including NVIDIA Jetson Nano, Intel Neural Compute Stick,
and Coral TPU. Using the proposed strategy, we achieved a peak accuracy of
99.49% when testing on the CK+ facial expression recognition dataset.
Additionally, we achieved a minimum inference latency of 0.39 milliseconds and
a minimum power consumption of 0.52 Watts.

本研究提出了一种分层框架，用于针对边缘部署开发和优化硬件感知的 CNN，并且在多种边缘 AI 加速器上进行全面的分析。使用该策略在 CK + 面部表情数据集上达到了 99.49% 的峰值准确度，并实现了 0.39 毫秒的最小推断延迟和 0.52 瓦特的最小功耗。