首页> 外文会议>FPGAs for Custom Computing Machines, 1998. Proceedings. IEEE Symposium on >The systolic array genetic algorithm, an example of systolic arrays as a reconfigurable design methodology
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The systolic array genetic algorithm, an example of systolic arrays as a reconfigurable design methodology

机译:脉动阵列遗传算法,作为可重构设计方法的脉动阵列示例

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We have designed and constructed a genetic algorithm engine using a systolic design methodology. The approach has a number of advantages. Firstly the design processes is systematic. A C source code version of the algorithm is used as a starting point and progressively the code is re-written into a form from where systolic cells can be designed. Secondly the modular nature of the arrays allow easy expansion of the design for different requirements (larger populations in this example). Hardware designs are re-used extensively and, in combination with reconfigurable computing techniques, can be swapped in or out on an application specific basis to construct arrays of the correct size. This can also be extended to swapping in and out whole elements of the macro-pipeline so that alternative operators, such as Tournament Selection can be employed. Thirdly, a traditional benefit of systolic arrays applies. The resultant design is massively parallel and significant throughput can be achieved.
机译:我们使用收缩设计方法设计并构建了遗传算法引擎。该方法具有许多优点。首先,设计过程是系统的。该算法的C源代码版本用作起点,并且逐渐将代码重新编写为可以设计收缩细胞的形式。其次,阵列的模块化特性使设计可以轻松扩展以适应不同的需求(在此示例中,数量更大)。硬件设计被大量重复使用,并且与可重新配置的计算技术结合使用,可以在特定于应用程序的基础上进行换入或换出,以构造正确大小的阵列。这也可以扩展为换入和换出宏管道的整个元素,以便可以使用其他的运算符,例如锦标赛选择。第三,应用了脉动阵列的传统优势。最终的设计在很大程度上是并行的,并且可以实现显着的吞吐量。

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