  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.
