Mobile or FPGA? A Comprehensive Evaluation on Energy Efficiency and a Unified Optimization Framework

Yuan, Geng; Dong, Peiyan; Sun, Mengshu; Niu, Wei; Li, Zhengang; Cai, Yuxuan; Li, Yanyu; Li, Jun; Jiang, Weiwen; Lin, Xue; Ren, Bin; Tang, Xulong; Wang, Yanzhi

doi:10.1145/3528578

Cited by 1 publication

(1 citation statement)

References 55 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This model was simple and lightweight. If the model was pruned and compressed, it would be suitable to use it to diagnose medical images on mobile devices [49,50].…”

Section: Resultsmentioning

confidence: 99%

An Interpretable CNN for the Segmentation of the Left Ventricle in Cardiac MRI by Real-Time Visualization

Li¹,

Yuan²,

Yang³

et al. 2023

Computer Modeling in Engineering &Amp; Sciences

View full text Add to dashboard Cite

The interpretability of deep learning models has emerged as a compelling area in artificial intelligence research. The safety criteria for medical imaging are highly stringent, and models are required for an explanation. However, existing convolutional neural network solutions for left ventricular segmentation are viewed in terms of inputs and outputs. Thus, the interpretability of CNNs has come into the spotlight. Since medical imaging data are limited, many methods to fine-tune medical imaging models that are popular in transfer models have been built using massive public ImageNet datasets by the transfer learning method. Unfortunately, this generates many unreliable parameters and makes it difficult to generate plausible explanations from these models. In this study, we trained from scratch rather than relying on transfer learning, creating a novel interpretable approach for autonomously segmenting the left ventricle with a cardiac MRI. Our enhanced GPU training system implemented interpretable global average pooling for graphics using deep learning. The deep learning tasks were simplified. Simplification included data management, neural network architecture, and training. Our system monitored and analyzed the gradient changes of different layers with dynamic visualizations in real-time and selected the optimal deployment model. Our results demonstrated that the proposed method was feasible and efficient: the Dice coefficient reached 94.48%, and the accuracy reached 99.7%. It was found that no current transfer learning models could perform comparably to the ImageNet transfer learning architectures. This model is lightweight and more convenient to deploy on mobile devices than transfer learning models.

show abstract

“…This model was simple and lightweight. If the model was pruned and compressed, it would be suitable to use it to diagnose medical images on mobile devices [49,50].…”

Section: Resultsmentioning

confidence: 99%