Energy efficiency in Edge TPU vs. embedded GPU for computer-aided
  medical imaging segmentation and classification
José María Rodríguez Corral Javier Civit-Masot Francisco Luna-Perejón Ignacio Díaz-Cano Arturo Morgado-Estévez Manuel Domínguez-Morales
In this work, we evaluate the energy usage of fully embedded medical
diagnosis aids based on both segmentation and classification of medical images
implemented on Edge TPU and embedded GPU processors. We use glaucoma diagnosis
based on color fundus images as an example to show the possibility of
performing segmentation and classification in real time on embedded boards and
to highlight the different energy requirements of the studied implementations.
  Several other works develop the use of segmentation and feature extraction
techniques to detect glaucoma, among many other pathologies, with deep neural
networks. Memory limitations and low processing capabilities of embedded
accelerated systems (EAS) limit their use for deep network-based system
training. However, including specific acceleration hardware, such as NVIDIA's
Maxwell GPU or Google's Edge TPU, enables them to perform inferences using
complex pre-trained networks in very reasonable times.
  In this study, we evaluate the timing and energy performance of two EAS
equipped with Machine Learning (ML) accelerators executing an example
diagnostic tool developed in a previous work. For optic disc (OD) and cup (OC)
segmentation, the obtained prediction times per image are under 29 and 43 ms
using Edge TPUs and Maxwell GPUs, respectively. Prediction times for the
classification subsystem are lower than 10 and 14 ms for Edge TPUs and Maxwell
GPUs, respectively. Regarding energy usage, in approximate terms, for OD
segmentation Edge TPUs and Maxwell GPUs use 38 and 190 mJ per image,
respectively. For fundus classification, Edge TPUs and Maxwell GPUs use 45 and
70 mJ, respectively.