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A scalable processor array for self-organizing feature maps

机译:用于自组织特征映射的可扩展处理器阵列

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Selforganizing Feature Maps (SOFMs) can be applied for data analysis, controlling problems and pattern matching. In many cases the requirements of a system using these maps are high performance and small physical size. This leads to the necessity of custom chip designs. In this paper two chips are presented, that realize a scalable processor array for self-organizing feature maps. First the design and test results of a single processor chip are described. Based on these results a second chip has been developed implementing a 5 by 5 array of elements. Each processor has on-chip memory to store 64 weights of 8 bit. The calculation unit has an internal precision of 14 bit. An input pattern can have 64 vector components of 8 bit. In order to achieve high speed, all elements work in parallel. Several of this chips can be cascaded to larger map sizes in a system.
机译:可以应用自主结构图(SOFMS)用于数据分析,控制问题和模式匹配。在许多情况下,使用这些地图的系统的要求是高性能和小物理尺寸。这导致了定制芯片设计的必要性。在本文中,提出了两个芯片,实现了用于自组织特征映射的可伸缩处理器阵列。首先,描述了单个处理器芯片的设计和测试结果。基于这些结果,已经开发了第二芯片,实现了5乘5个元件。每个处理器都有片上存储器,可以存储64重的8位。计算单元具​​有14位的内部精度。输入图案可以具有8位的64个矢量分量。为了实现高速,所有元素并行工作。其中几个芯片可以在系统中级联到较大的地图尺寸。

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