| (Discrete) Fast Fourier Transform implementations: https://fftw.org/ ; FFTW: https://en.wikipedia.org/wiki/FFTW gh topic: fftw: https://github.com/topics/fftw xtensor-stack/xtensor-fftw is similar to numpy.fft:
https://github.com/xtensor-stack/xtensor-fftw Nvidia CuFFTW, and/amd-fftw, Intel MKL FFTW NVIDIA CuFFT (GPU FFT)
https://docs.nvidia.com/cuda/cufft/index.html ROCm/rocFFT (GPU FFT)
https://github.com/ROCm/rocFFT .. docs: https://rocm.docs.amd.com/projects/rocFFT/en/latest/ AMD FFT, Intel FFT: https://www.google.com/search?q=AMD+FFT , https://www.google.com/search?q=Intel+FFT project-gemmi/benchmarking-fft:
https://github.com/project-gemmi/benchmarking-fft "An FFT Accelerator Using Deeply-coupled RISC-V Instruction Set Extension for Arbitrary Number of Points" (2023) https://ieeexplore.ieee.org/document/10265722 : > with data loading from either specially designed vector registers (V-mode) or RAM off-the-core (R-mode). The evaluation shows the proposed FFT acceleration scheme achieves a performance gain of 118 times in V-mode and 6.5 times in R-mode respectively, with only 16% power consumption required as compared to the vanilla NutShell RISC-V microprocessor "CSIFA: A Configurable SRAM-based In-Memory FFT Accelerator" (2024)
https://ieeexplore.ieee.org/abstract/document/10631146 /? dsp hardware FFT: https://www.google.com/search?q=dsp+hardware+fft |