Memory-Efficient Training with In-Place FFT Implementation

arXiv — cs.LG•Tuesday, November 4, 2025 at 5:00:00 AM

A new framework for Fast Fourier Transforms (FFT) has been introduced, which significantly improves memory efficiency in deep learning applications. This innovative real-domain, fully in-place FFT (rdFFT) addresses the limitations of existing implementations that struggle with dimensional mismatches and excessive memory allocation. By optimizing the computation process, this advancement not only enhances performance but also opens up new possibilities for more efficient deep learning models, making it a noteworthy development in the field.

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