Realtime Facial Expression Recognition: Neuromorphic Hardware vs. Edge
  AI Accelerators
Heath Smith James Seekings Mohammadreza Mohammadi Ramtin Zand
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.