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Systolic Gaussian Normal Basis Multiplier Architectures Suitable for High-Performance Applications

机译:适用于高性能应用的脉动高斯正态基础乘法器体系结构

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Normal basis multiplication in finite fields is vastly utilized in different applications, including error control coding and the like due to its advantageous characteristics and the fact that squaring of elements can be obtained without hardware complexity. In this brief, we present decomposition algorithms to develop novel systolic structures for digit-level Gaussian normal basis multiplication over GF. The proposed architectures are suitable for high-performance applications, which require fast computations in finite fields with high throughputs. We also present the results of our application-specific integrated circuit synthesis using a 65-nm standard-cell library to benchmark the effectiveness of the proposed systolic architectures. The presented architectures for multiplication can result in more efficient and high-performance VLSI systems.
机译:由于其有利的特性以及可以在不增加硬件复杂性的情况下获得元素平方的事实,有限域中的普通基乘法在包括差错控制编码等在内的不同应用中得到了广泛应用。在本文中,我们提出了分解算法,以开发用于GF上的数字级高斯正态基础乘法的新型收缩结构。所提出的体系结构适用于高性能应用,这些应用需要在有限的领域中以高吞吐量进行快速计算。我们还介绍了使用65纳米标准单元库的专用集成电路综合结果,以对所提出的脉动架构的有效性进行基准测试。提出的用于乘法的体系结构可以产生更高效和高性能的VLSI系统。

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