Automatic Recognition of Ocular Surface Diseases on Smartphone Images Using Densely Connected Convolutional Networks

Chen, Rong; Zeng, Wankang; Fan, Wenkang; Lai, Fang; Chen, Yinran; Lin, Xiang; Tang, Liying; Ouyang, Weijie; Liu, Zuguo; Luop, Xiongbiao

doi:10.1109/embc46164.2021.9630359

Cited by 5 publications

(1 citation statement)

References 12 publications

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“…The MobileNetV3 network architecture can be divided into four main memristor-based neural network units, in which the composition and connection relationship are shown in Figure 1. The computing paradigm has the same neural network layers as (Chen et al, 2021). The input layer is a preprocessing unit for input images, including a convolutional sub-layer, a normalization sub-layer, and a hard swish activation sub-layer.…”

Section: Network Architecturementioning

confidence: 99%

Memristor-Based In-Memory Computing Architecture for Scientific Computing

Li²,

Ling³

et al. 2022

Memristor Computing Systems

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The advancement in the field of machine learning is inextricably linked with the concurrent progress in domain-specific hardware accelerators such as GPUs and TPUs. However, the rapidly growing computational demands necessitated by larger models and increased data have become a primary bottleneck in further advancing machine learning, especially in mobile and edge devices. Currently, the neuromorphic computing paradigm based on memristors presents a promising solution. In this study, we introduce a memristorbased MobileNetV3 neural network computing paradigm and provide an end-to-end framework for validation. The results demonstrate that this computing paradigm achieves over 90% accuracy on the CIFAR-10 dataset while saving inference time and reducing energy consumption. With the successful development and verification of MobileNetV3, the potential for realizing more memristor-based neural networks using this computing paradigm and open-source framework has significantly increased. This progress sets a groundbreaking pathway for future deployment initiatives.

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Section: Network Architecturementioning

confidence: 99%