文章基本信息

标题：A Programmable Look-Up Table-Based Interpolator with Nonuniform Sampling Scheme
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作者：Élvio Carlos Dutra e Silva Júnior ; Leandro Soares Indrusiak ; Weiler Alves Finamore 等
期刊名称：International Journal of Reconfigurable Computing
印刷版ISSN：1687-7195
电子版ISSN：1687-7209
出版年度：2012
卷号：2012
DOI：10.1155/2012/647805
出版社：Hindawi Publishing Corporation
摘要：Interpolation is a useful technique for storage of complex functions on limited memory space: some few sampling values are stored on a memory bank, and the function values in between are calculated by interpolation. This paper presents a programmable Look-Up Table-based interpolator, which uses a reconfigurable nonuniform sampling scheme: the sampled points are not uniformly spaced. Their distribution can also be reconfigured to minimize the approximation error on specific portions of the interpolated function’s domain. Switching from one set of configuration parameters to another set, selected on the fly from a variety of precomputed parameters, and using different sampling schemes allow for the interpolation of a plethora of functions, achieving memory saving and minimum approximation error. As a study case, the proposed interpolator was used as the core of a programmable noise generator—output signals drawn from different Probability Density Functions were produced for testing FPGA implementations of chaotic encryption algorithms. As a result of the proposed method, the interpolation of a specific transformation function on a Gaussian noise generator reduced the memory usage to 2.71% when compared to the traditional uniform sampling scheme method, while keeping the approximation error below a threshold equal to 0.000030518.