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。