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Energy efficiency in Edge TPU vs. embedded GPU for computer-aided medical imaging segmentation and classification by Corral, José María Rodríguez; Civit-Masot, Javier; Luna-Perejón, Francisco; Díaz-Cano, Ignacio; Morgado-Estévez, Arturo; Domínguez-Morales, Manuel is a scholarly article available to read on EtoBox.

What is Energy efficiency in Edge TPU vs. embedded GPU for computer-aided medical imaging segmentation and classification about?

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

Author

Corral, José María Rodríguez; Civit-Masot, Javier; Luna-Perejón, Francisco; Díaz-Cano, Ignacio; Morgado-Estévez, Arturo; Domínguez-Morales, Manuel

Published

2023

Language

EN