论文标题
现在我可以看到，我可以改进：在边缘启用CNN的数据驱动填充
Now that I can see, I can improve: Enabling data-driven finetuning of CNNs on the edge
论文作者
论文摘要
在当今世界，边缘设备正在生成大量数据，这些数据可以用作有价值的培训数据，以改善机器学习算法的性能，以实现的准确性或减少模型的计算要求。但是，由于用户数据隐私问题以及存储和通信带宽的限制，该数据无法从设备转移到数据中心，以进一步改进模型和随后的部署。因此，需要增加边缘智能，其中可以对部署的模型进行微调，从而提高准确性和/或减少模型的工作量以及其内存和功率足迹。在卷积神经网络（CNN）的情况下，可以调整网络及其拓扑的权重以适应其处理的数据。本文提供了基于结构化修剪在边缘设备上启用CNN Finetuning的第一步。它探索了这样做的性能增长和成本，并提出了可扩展的开源框架，该框架允许在各种网络架构和设备上部署此类方法。结果表明，平均而言，使用RETRANENing进行数据感知可以提供10.2pp在广泛的子集，网络和修剪水平上提高准确性，而最大提高了42.0pp，而在修剪和再培训中，以一种对网络处理的数据的不可知和重新培训。
In today's world, a vast amount of data is being generated by edge devices that can be used as valuable training data to improve the performance of machine learning algorithms in terms of the achieved accuracy or to reduce the compute requirements of the model. However, due to user data privacy concerns as well as storage and communication bandwidth limitations, this data cannot be moved from the device to the data centre for further improvement of the model and subsequent deployment. As such there is a need for increased edge intelligence, where the deployed models can be fine-tuned on the edge, leading to improved accuracy and/or reducing the model's workload as well as its memory and power footprint. In the case of Convolutional Neural Networks (CNNs), both the weights of the network as well as its topology can be tuned to adapt to the data that it processes. This paper provides a first step towards enabling CNN finetuning on an edge device based on structured pruning. It explores the performance gains and costs of doing so and presents an extensible open-source framework that allows the deployment of such approaches on a wide range of network architectures and devices. The results show that on average, data-aware pruning with retraining can provide 10.2pp increased accuracy over a wide range of subsets, networks and pruning levels with a maximum improvement of 42.0pp over pruning and retraining in a manner agnostic to the data being processed by the network.
