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Tiled Architecture of a CNN-mostly IP System

机译:CNN为主的IP系统的平铺架构

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Multi-core architectures have been popularized with the advent of the IBM CELL. On a finer grain the problems in scheduling multi-cores have already existed in the tiled architectures, such as the EPIC and Da Vinci. It is not easy to evaluate the performance of a schedule on such architecture as historical data are not available. One solution is to compile algorithms for which an optimal schedule is known by analysis. A typical example is an algorithm that is already defined in terms of many collaborating simple nodes, such as a Cellular Neural Network (CNN). A simple node with a local register stack together with a 'rotating wheel' internal communication mechanism has been proposed. Though the basic CNN allows for a tiled implementation of a tiled algorithm on a tiled structure, a practical CNN system will have to disturb this regularity by the additional need for arithmetical and logical operations. Arithmetic operations are needed for instance to accommodate for low-level image processing, while logical operations are needed to fork and merge different data streams without use of the external memory. It is found that the 'rotating wheel' internal communication mechanism still handles such mechanisms without the need for global control. Overall the CNN system provides for a practical network size as implemented on a FPGA, can be easily used as embedded IP and provides a clear benchmark for a multi-core compiler.
机译:随着IBM CELL的出现,多核体系结构已经普及。从更细微的角度来看,诸如EPIC和Da Vinci之类的平铺体系结构中已经存在调度多核的问题。由于无法获得历史数据,因此很难在这种体系结构上评估计划的性能。一种解决方案是编译通过分析已知最佳调度的算法。一个典型的示例是一种已经根据许多协作简单节点(如细胞神经网络(CNN))定义的算法。已经提出了具有本地寄存器栈以及“旋转轮”内部通信机制的简单节点。尽管基本的CNN允许在分片结构上分片实施分片算法,但实际的CNN系统将不得不通过对算术和逻辑运算的额外需求来扰乱这种规律性。例如,需要算术运算来适应低级图像处理,而需要逻辑运算来分叉和合并不同的数据流而无需使用外部存储器。已经发现,“旋转轮”内部通信机制仍然可以处理这种机制,而无需全局控制。总体而言,CNN系统提供了在FPGA上实现的实际网络规模,可以轻松地用作嵌入式IP,并为多核编译器提供了明确的基准。

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