Effective and efficient routing is one of the most important parts of routing in NoC-based neuromorphic systems. In fact, this communication structure connects different units through the packets routed by routers and switches embedded in the network on a chip. With the help of this capability, not only high scalability and high development can be created, but by decreasing the global wiring to the chip level, power consumption can be reduced. In this paper, an adaptive routing algorithm for NoC-based neuromorphic systems is proposed along with a hybrid selection strategy. Accordingly, a traffic analyzer is first used to determine the type of local or nonlocal traffic depending on the number of hops. Then, considering the type of traffic, the RCA and NoP selection strategies are used for the nonlocal and local strategies, respectively. Finally, using the experiments that performed in the simulator environment, it has been shown that this solution can well reduce the average delay time and power consumption.

中文翻译：

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提供自适应路由以及混合选择策略以提高基于 NoC 的神经形态系统的效率

有效且高效的路由是基于 NoC 的神经形态系统中路由的最重要部分之一。事实上，这种通信结构是通过芯片上嵌入网络的路由器和交换机路由的数据包连接不同的单元。借助这种能力，不仅可以创造高扩展性和高开发性，而且通过将全局布线减少到芯片级，还可以降低功耗。在本文中，提出了一种基于 NoC 的神经形态系统的自适应路由算法以及混合选择策略。因此，流量分析器首先用于根据跳数确定本地或非本地流量的类型。然后，考虑到流量的类型，RCA 和 NoP 选择策略分别用于非本地和本地策略。最后，