This paper presents an energy-efficient short-time Fourier transform (STFT) architecture. The proposed architecture is called frequency decomposition STFT (FD-STFT) and it achieves significant computational complexity reduction by effectively re-utilizing previously computed spectrums between overlapped sampling windows. Such an algorithmic modification not only reduces the required hardware units, but also achieves low accumulative error compared to conventional approaches. In addition, the quality of the resulting spectrogram is improved by integrating an efficient hanning windowing technique that replaces the multiplication in the time domain, with a low-cost filtering in the frequency domain. For an $N$-point window with $R$ overlapping samples, the proposed architecture requires 3N -2N/R memory cells, (2N/R)(log(R)+1) multipliers and (2N/R)(log(R)+1) adders, while achieving up-to 40.86% and 65.56% area and power savings respectively, for hop size R=32, compared to recent approaches.
This paper presents an energy-efficient short-time Fourier transform (STFT) architecture. The proposed architecture is called frequency decomposition STFT (FD-STFT) and it achieves significant computational complexity reduction by effectively re-utilizing previously computed spectrums between overlapped sampling windows. Such an algorithmic modification not only reduces the required hardware units, but also achieves low accumulative error compared to conventional approaches. In addition, the quality of the resulting spectrogram is improved by integrating an efficient hanning windowing technique that replaces the multiplication in the time domain, with a low-cost filtering in the frequency domain. For an $N$-point window with $R$ overlapping samples, the proposed architecture requires 3N -2N/R memory cells, (2N/R)(log(R)+1) multipliers and (2N/R)(log(R)+1) adders, while achieving up-to 40.86% and 65.56% area and power savings respectively, for hop size R=32, compared to recent approaches. Read More
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