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High-Level Plots neu

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Patrick Hangl
2026-02-27 12:27:50 +01:00
parent 9651256442
commit 30b3376676
11 changed files with 193 additions and 14 deletions
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chapter_04.tex
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\section{Hardware implementation and optimization of the ANR Algorithm on a low-power system}
\section{Hardware implementation and performance quantization of the ANR Algorithm on a low-power system}
This section can be considered as the heart of this thesis. The first subchapter describes the hardware, on which the \ac{ANR} algorithm is implemented, including its environment, which serves as a link to the \ac{CI} system itself. The following subchapter continues with the basic implementation of the \ac{ANR} algorithm on the hardware itself and shall provide the reader with a basic understanding of its challenges, possibilities and limitations. This basic implementation is then low-level simulated with some of the previuous use cases to get some idea of the general performance.\\
The last subchapter picks the final optimizations of the \ac{ANR} algorithm itself as a central theme, especially with respect to the capabilities of a hybrid \ac{ANR} approach.
\subsection{Low-power system architecture and integration}
@@ -262,8 +262,6 @@ C_{write\_output} = 5 \\
C_{total} = N + \frac{6*N+8}{U} + 34
\end{equation}
Equation \ref{equation_computing_final} now provides an estimation of the necessary computing effort for one output sample in relation to the filter length $N$ and the update rate of the filter coefficients $1/U$. This formula can now be used to estimate the needed computing power (and therefore the power consumption) of the \ac{DSP} core for different parameter settings, alowing to find an optimal parameter configuration in regard of the quality of the noise reduction and the power consumption of the system.
\subsubsection{Performance evaluation of different implementation variants}
To verify the general performance of the \ac{DSP} implemented \ac{ANR} algorithm, the complex usecase of the high-level implemenation is used, which includes a 16-tap \ac{FIR} filter and an update of the filter coefficients every cycle. In contary to the high-level implementation, the coeffcient convergence is now not included in the evaluation anymore, but the metric for the \ac{ANR} performance stays the same as the \ac{SNR} improvement