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![modelsim iteration limit reached modelsim iteration limit reached](https://www.researchgate.net/publication/333439518/figure/fig5/AS:960267768836102@1605957085328/Path-planning-results-synthesized-by-FBAS-algorithm-to-avoid-multi-regular-obstacle-a.png)
In certain contexts, achieving elapsed algorithm times in the order of seconds as opposed to minutes may yield a substantial impact on the application. Even though such regularization allows the problem to be solved in a computationally efficient manner (usually associated to a complexity which is proportional to a polynomial function of the number of inputs), the fact that a computer can solve the problem does not necessarily mean that the result is achieved quickly, practically speaking. Such an approach is required for highly reliable estimation results. For that reason, several different methodologies have been proposed in the literature, with the ones that make use of ℓ 1 regularization to counter the problem’s inherent high-dimensionality arguably figuring as the most successful ones. Trend break detection in the presence of noise is a broad problem that can be found across different research fields. The proposed architecture is compared with a state-of-the-art hardware structure for sparse estimation, and the results indicate that its performance concerning trend break detection is much more pronounced while, at the same time, being the indicated solution for long datasets. This represents a tremendous advantage in using a dedicated unit for trend break detection applications.
Modelsim iteration limit reached software#
The hardware is synthesized in different-sized FPGAs, and the percentage of used hardware, as well as the maximum frequency enabled by the design, indicate that an approximately 100 gain factor in processing time, concerning the software implementation, can be achieved. In this work, a hardware architecture of the Linearized Bregman Iteration algorithm is presented and tested on a Field Programmable Gate Array (FPGA). The Linearized Bregman Iterations have been recently presented as a low-complexity and computationally efficient algorithm to tackle this problem, with a formidable structure that could benefit immensely from hardware implementation. Although many algorithms have been developed to deal with such a problem, accurate and low-complexity trend break detection is still an active topic of research. Detection of level shifts in a noisy signal, or trend break detection, is a problem that appears in several research fields, from biophysics to optics and economics.