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Public welfare organization management system based on FPGA and deep learning

机译：基于FPGA和深度学习的公共福利组织管理系统

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Public welfare organization system Promoting social support is one of the government's influence relieving benevolent associations. To qualify as exempt organizations, community or government support, the association exercises should simply replace the network edge, welfare organization is conducive to select another person, and every person should be registering for details. The social ground considered as network partner to the exercise of social, government support element in any case, despite all the affiliation, if the network is advantageous. Costs associated social modernization typically does not include support groups. Long-term support will be used to understand the impact of decisions and related efforts in the political mission exercises. So, the government's social support groups altruistic relationship. A Field-Programmable Gate Array (FPGA), and a Graphics Processing Unit (GPU) to improve the throughput of the cellular neural network. Rather, Field Programmable Gate Array (FPGA) accelerating the depth learning network is not just one reason, but also because of its ability in energy efficiency, the maximum parallelism. In this article, we review recent prior art deeply accelerated learning networks in the FPGA. We stress the importance of using a variety of techniques to improve the acceleration performance of important features. We also offer suggestions for improving the use of FPGA accelerated cellular neural networks. In this paper, research methods represent FPGA-based accelerator's recent trends in depth learning networks. Therefore, this review is expected to be useful in depth study researchers' direct and efficient hardware accelerator future development.
机译：促进社会支持的公共福利组织制度是政府影响仁慈协会的影响之一。为了获得豁免组织，社区或政府支持，协会练习应该简单地取代网络边缘，福利组织有利于选择另一个人，每个人都应该注册详情。社交场合被认为是网络合作伙伴在任何情况下行使社会，政府支持元素，尽管所有的关系都是有利的。成本相关的社会现代化通常不包括支持群体。长期支持将用于了解决策和相关努力在政治使命练习中的影响。因此，政府的社会支持群体利他关系。现场可编程门阵列（FPGA）和图形处理单元（GPU），以提高蜂窝神经网络的吞吐量。相反，现场可编程门阵列（FPGA）加速深度学习网络不仅仅是一个原因，而且因为它的能效，最大并行性。在本文中，我们审查了最近的现有技术在FPGA中深入加速了学习网络。我们强调了利用各种技术来提高重要特征​​的加速性能的重要性。我们还提供改善FPGA加速蜂窝神经网络的使用的建议。在本文中，研究方法代表了基于FPGA的加速器最近深度学习网络的趋势。因此，预计本综述将在深入研究研究人员的直接和高效的硬件加速器未来发展中有用。

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