遗传编程在FPGA中实现加法作为卷积的一部分

E. Jamro, K. Wiatr
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引用次数: 11

摘要

在fpga中,加法应该以使用波纹进位加法器(而不是进位节省加法器)的标准方式进行,这使得寻找最佳加法器结构变得复杂,因为路由顺序对加法成本有实质性影响。此外,还考虑了加法器块输入的复杂参数,例如输入之间的相关性。对于不同的卷积器体系结构,这些参数以不同的方式指定。因此,加法器树的优化是本文讨论的一个关键问题。提出了模拟退火算法和遗传规划算法,并与贪心算法(GrA)和穷举搜索算法(ES)进行了比较。因此,当计算时间非常重要时,GrA是最佳解决方案。否则,对于添加输入数N>8,建议采用模拟退火,对于N/spl les/8,建议采用ES。与GrA相比,采用模拟退火可以减少约10-20%的面积。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Genetic programming in FPGA implementation of addition as a part of the convolution
In FPGAs, an addition should be carried out in the standard way employing ripple-carry adders (rather than carry-save adders), which complicates search for an optimal adder structure as routing order has a substantial influence on the addition cost. Further, complex parameters of inputs to the adder block have been considered e.g. correlation between inputs. These parameters are specified in different ways for different convolver architectures. Consequently optimisation of the adder tree is a key issue addressed in this paper. Simulated Annealing and Genetic Programming have been proposed, and obtained results compared with the Greedy Algorithm (GrA) and the Exhaustive Search (ES). As a result, the GrA is the best solution when computation time is of great importance. Otherwise, the Simulated Annealing should be employed for the number of addition inputs N>8, and the ES is recommended for N/spl les/8. Employing the Simulated Annealing gives about 10-20% area reduction in comparison to the GrA.
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