Q-Memory Task Routing to Prevent Deadlocks in Ethernet Control with Memory Crossbar Switching

Smita Sudhakar Palnitkar, Sudhir Kanade
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Abstract

In Ethernet system, as a result of head of line blocking, numerous control data queues with high priority may cause priority queues to become overcrowded and their receiving DMAs (Direct Memory Access) to run out of buffer space, forcing them to delete packets that are still arriving from the network. Thus the primary goal of this work is to prevent deadlock in an Ethernet system while sending congested information across the Ethernet protocol and channel. In order to allow many processors to interact concurrently without causing a conflict, this research paper proposes a Memory crossbar switching control in which the memory is divided into global and local partitions utilizing the q-learning architecture in the development of a Q-Memory task routing architecture. The path average value therefore represents congestion information for each router and its surrounding nodes. The nearby router receives the path average value if the message is received. The networks-on-chip protocol and channel should be used to provide congestion information in order to prevent deadlock in a system and improve communication.

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利用 Q-Memory 任务路由防止以太网控制中的死锁与内存交叉条交换
摘要 在以太网系统中,由于线路头部阻塞,众多具有高优先级的控制数据队列可能会导致优先级队列拥挤不堪,其接收 DMA(直接内存访问)的缓冲空间耗尽,迫使它们删除仍在从网络到达的数据包。因此,这项工作的主要目标是防止以太网系统出现死锁,同时通过以太网协议和通道发送拥挤的信息。为了让许多处理器同时交互而不造成冲突,本研究论文提出了一种内存跨条切换控制,在这种控制中,内存被分为全局和局部分区,在开发 Q-Memory 任务路由架构时利用了 q-learning 架构。因此,路径平均值代表了每个路由器及其周围节点的拥塞信息。如果收到信息,附近的路由器就会收到路径平均值。应利用片上网络协议和信道提供拥塞信息,以防止系统出现死锁并改善通信。
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来源期刊
CiteScore
1.50
自引率
11.10%
发文量
25
期刊介绍: The journal covers a wide range of issues in information optics such as optical memory, mechanisms for optical data recording and processing, photosensitive materials, optical, optoelectronic and holographic nanostructures, and many other related topics. Papers on memory systems using holographic and biological structures and concepts of brain operation are also included. The journal pays particular attention to research in the field of neural net systems that may lead to a new generation of computional technologies by endowing them with intelligence.
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