• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>IEEE International Symposium on Real-Time Distributed Computing >A Cross-Layer Review of Deep Learning Frameworks to Ease Their Optimization and Reuse
【24h】

A Cross-Layer Review of Deep Learning Frameworks to Ease Their Optimization and Reuse

机译：深度学习框架的跨层审查，以简化其优化和重用

获取原文
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Machine learning and especially Deep Learning (DL) approaches are at the heart of many domains, from computer vision and speech processing to predicting trajectories in autonomous driving and data science. Those approaches mainly build upon Neural Networks (NNs), which are compute-intensive in nature. A plethora of frameworks, libraries and platforms have been deployed for the implementation of those NNs, but end users often lack guidance on what frameworks, platforms and libraries to use to obtain the best implementation for their particular needs. This paper analyzes the DL ecosystem providing a structured view of some of the main frameworks, platforms and libraries for DL implementation. We show how those DL applications build ultimately on some form of linear algebra operations such as matrix multiplication, vector addition, dot product and the like. This analysis allows understanding how optimizations of specific linear algebra functions for specific platforms can be effectively leveraged to maximize specific targets (e.g. performance or power-efficiency) at application level reusing components across frameworks and domains.
机译：机器学习，尤其是深度学习（DL）方法是许多领域的核心，从计算机视觉和语音处理到自动驾驶和数据科学中的预测轨迹。这些方法主要建立在神经网络（NNs）上，而神经网络本质上是计算密集型的。已经部署了许多框架，库和平台来实现这些NN，但是最终用户通常缺乏有关使用哪种框架，平台和库来获得针对其特定需求的最佳实现的指南。本文分析了DL生态系统，为DL实现提供了一些主要框架，平台和库的结构化视图。我们将展示这些DL应用程序如何最终建立在某种形式的线性代数运算上，例如矩阵乘法，向量加法，点积等。通过这种分析，您可以了解如何有效利用特定平台的特定线性代数函数的优化，以在框架和域之间重用组件的应用程序级别最大化特定目标（例如性能或功效）。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号