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

• URL: http://arxiv.org/abs/2311.12876v1
• Date: Mon, 20 Nov 2023 09:38:56 GMT
• Title: Energy efficiency in Edge TPU vs. embedded GPU for computer-aided medical imaging segmentation and classification
• Authors: Jos\'e Mar\'ia Rodr\'iguez Corral, Javier Civit-Masot, Francisco Luna-Perej\'on, Ignacio D\'iaz-Cano, Arturo Morgado-Est\'evez, Manuel Dom\'inguez-Morales
• Abstract summary: 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. Memory limitations and low processing capabilities of embedded accelerated systems (EAS) limit their use for deep network-based system training. 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.
• Score: 0.9728436272434581
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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