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. 1. Introduction Nowadays, the world is facing a boom on the fusion between telecommunications and information technology. The merging of these two fields spreads over all kinds of information systems, requiring efforts for ensuring the integration among many kinds of organizations [1], from tactical to strategic operations, in different levels of information system interoperability [2]. The ISO/OSI seven-layer model arises as a lighthouse for seeking the interoperability on many different layers of networked solutions [3]. Many standards and protocols arise from this model, including cryptographic ones. Encryption solutions can be implemented on both software and hardware. Software implementations are more related to the protection of the information itself, while hardware ones can be also used to protect the communication channels [4]. In the case of tactical telecommunication systems, which require both channel and information security, the hardware implementation of such encryption algorithms arises as a better compromise. The need to test the behavior of such systems against different sources of noise and jamming becomes the motivation to implement, on FPGA (Field-Programmable Gate Array), a programmable noise generator. A
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