论文标题
回忆计算，以有效推断资源约束设备
Memristive Computing for Efficient Inference on Resource Constrained Devices
论文作者
论文摘要
深度学习的出现导致了许多应用程序，这些应用改变了已应用的研究领域的景观。但是，随着受欢迎程度的提高，多年来，经典深层神经网络的复杂性有所增加。结果，这导致在具有空间和时间限制的设备上部署期间有很大的问题。在这项工作中，我们对非易失性记忆中目前的进步进行了综述，以及使用电阻RAM记忆，尤其是回忆录的使用如何有助于进步深度学习中的研究状态。换句话说，我们希望提出一种意识形态，即记忆技术领域的进步可以极大地影响和影响对边缘设备的深度学习推断。
The advent of deep learning has resulted in a number of applications which have transformed the landscape of the research area in which it has been applied. However, with an increase in popularity, the complexity of classical deep neural networks has increased over the years. As a result, this has leads to considerable problems during deployment on devices with space and time constraints. In this work, we perform a review of the present advancements in non-volatile memory and how the use of resistive RAM memory, particularly memristors, can help to progress the state of research in deep learning. In other words, we wish to present an ideology that advances in the field of memristive technology can greatly influence and impact deep learning inference on edge devices.
