Title: Deep Compressed Pneumonia Detection for Low-Power Embedded Devices Show Chinese title

Title: 低功耗嵌入式设备的深度压缩肺炎检测

Abstract: Deep neural networks (DNNs) have been expanded into medical fields and triggered the revolution of some medical applications by extracting complex features and achieving high accuracy and performance, etc. On the contrast, the large-scale network brings high requirements of both memory storage and computation resource, especially for portable medical devices and other embedded systems. In this work, we first train a DNN for pneumonia detection using the dataset provided by RSNA Pneumonia Detection Challenge. To overcome hardware limitation for implementing large-scale networks, we develop a systematic structured weight pruning method with filter sparsity, column sparsity and combined sparsity. Experiments show that we can achieve up to 36x compression ratio compared to the original model with 106 layers, while maintaining no accuracy degradation. We evaluate the proposed methods on an embedded low-power device, Jetson TX2, and achieve low power usage and high energy efficiency. Show Chinese abstract

Abstract: 深度神经网络（DNN）已被扩展到医学领域，并通过提取复杂特征、实现高精度和高性能等，触发了一些医学应用的革命。 相比之下，大规模网络对内存存储和计算资源提出了很高的要求，特别是对于便携式医疗设备和其他嵌入式系统而言。 在这项工作中，我们首先使用RSNA肺炎检测挑战赛提供的数据集训练了一个用于肺炎检测的DNN。 为了克服实现大规模网络的硬件限制，我们开发了一种具有滤波器稀疏性、列稀疏性和组合稀疏性的系统化结构权重剪枝方法。 实验表明，与具有106层的原始模型相比，我们可以实现高达36倍的压缩比，同时保持无精度损失。 我们在嵌入式低功耗设备Jetson TX2上评估了所提出的方法，实现了低功耗和高能效。