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.

公益组织体系 推动社会救助是政府救济慈善团体的影响力之一。要获得豁免组织、社区或政府支持的资格，协会活动应简单地取代网络边缘，福利组织有利于选择其他人，并且每个人都应登记详细信息。在任何情况下，如果网络有利，则无论有多少隶属关系，社会基础都被视为行使社会、政府支持要素的网络合作伙伴。与社会现代化相关的成本通常不包括支持团体。长期支持将用于了解政治任务演习中决策和相关努力的影响。所以，政府对社会支持群体是利他关系。现场可编程门阵列 (FPGA) 和图形处理单元 (GPU)，用于提高细胞神经网络的吞吐量。相反，现场可编程门阵列（FPGA）加速深度学习网络不仅仅是原因之一，还因为它在能源效率、最大并行性方面的能力。在本文中，我们回顾了 FPGA 中最新的深度加速学习网络的现有技术。我们强调使用各种技术来提高重要功能的加速性能的重要性。我们还提供改进 FPGA 加速细胞神经网络使用的建议。本文的研究方法代表了基于 FPGA 加速器的深度学习网络的最新趋势。因此，本文的综述预计将有助于深入研究研究人员直接高效的硬件加速器的未来发展。