• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>ACM Computing Surveys >Deep Learning on Mobile and Embedded Devices:State-of-the-art, Challenges, and Future Directions
【24h】

Deep Learning on Mobile and Embedded Devices:State-of-the-art, Challenges, and Future Directions

机译：在移动和嵌入式设备上深入学习：最先进的，挑战和未来的方向

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Recent years have witnessed an exponential increase in the use of mobile and embedded devices. With the great success of deep learning in many fields, there is an emerging trend to deploy deep learning on mobile and embedded devices to better meet the requirement of real-time applications and user privacy protection. However, the limited resources of mobile and embedded devices make it challenging to fulfill the intensive computation and storage demand of deep learning models. In this survey, we conduct a comprehensive review on the related issues for deep learning on mobile and embedded devices. We start with a brief introduction of deep learning and discuss major challenges of implementing deep learning models on mobile and embedded devices. We then conduct an in-depth survey on important compression and acceleration techniques that help adapt deep learning models to mobile and embedded devices, which we specifically classify as pruning, quantization, model distillation, network design strategies, and low-rank factorization. We elaborate on the hardware-based solutions, including mobile GPU, FPGA, and ASIC, and describe software frameworks for mobile deep learning models, especially the development of frameworks based on OpenCL and RenderScript. After that, we present the application of mobile deep learning in a variety of areas, such as navigation, health, speech recognition, and information security. Finally, we discuss some future directions for deep learning on mobile and embedded devices to inspire further research in this area.
机译：近年来目睹了使用移动和嵌入式设备的指数增加。随着许多领域的深度学习的巨大成功，有一个新兴趋势，可以在移动和嵌入式设备上部署深度学习，以更好地满足实时应用和用户隐私保护的要求。然而，移动和嵌入式设备的资源有限使其充满挑战，以满足深入学习模型的密集计算和存储需求。在本调查中，我们对移动和嵌入式设备的深度学习相关问题进行了全面审查。我们从简要介绍深入学习，并探讨在移动和嵌入式设备上实施深度学习模式的重大挑战。然后，我们对重要的压缩和加速技术进行了深入的调查，帮助将深度学习模型适应移动和嵌入式设备，我们将其特异性分类为修剪，量化，模型蒸馏，网络设计策略和低级别分解。我们详细说明了基于硬件的解决方案，包括移动GPU，FPGA和ASIC，并描述了移动深度学习模型的软件框架，特别是基于OpenCL和RenderScript的框架的开发。之后，我们展示了移动深度学习在各种领域的应用，例如导航，健康，语音识别和信息安全。最后，我们讨论了在移动和嵌入式设备上深入学习的未来方向，以激发该领域的进一步研究。

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