eFFT

We introduce eFFT, an efficient method for the calculation of the exact Fourier transform of an asynchronous event stream. It is based on keeping the matrices involved in the Radix-2 FFT algorithm in a tree data structure and updating them with the new events, extensively reusing computations, and avoiding unnecessary calculations while preserving exactness. eFFT can operate event-by-event, requiring for each event only a partial recalculation of the tree since most of the stored data are reused. It can also operate with event packets, using the tree structure to detect and avoid unnecessary and repeated calculations when integrating the different events within each packet to further reduce the number of operations.

⚙️ Installation

eFFT is provided as a header-only file for easy integration and relies solely on C++ standard and Eigen3 libraries.

Note

FFTW3 served as the benchmark for testing and evaluation. To enable it, define EFFT_USE_FFTW3 during compilation (e.g., -DEFFT_USE_FFTW3).

🖥️ Usage

Here's a minimal working example:

eFFT<128> efft;           // Instance
efft.initialize();        // Initialization
 
Stimulus e(1, 1, true);   // Event insertion
efft.update(e);           // Insert event
 
efft.getFFT();            // Get result as Eigen matrix

And another example handling event packets:

eFFT<128> efft;                   // Instance
efft.initialize();                // Initialization
 
Stimuli events;
events.emplace_back(1, 1, true);  // Insert event
events.emplace_back(2, 2, true);  // Insert event
events.emplace_back(3, 3, false); // Extract event
efft.update(events);              // Insert event
 
efft.getFFT();                    // Get result as Eigen matrix

Please refer to the official documentation for more details.

📜 Citation

If you use this work in an academic context, please cite the following publication:

‍R. Tapia, J.R. Martínez-de Dios, A. Ollero eFFT: An Event-based Method for the Efficient Computation of Exact Fourier Transforms, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024.

@article{tapia2024efft,
  author={Tapia, R. and Martínez-de Dios, J.R. and Ollero, A.},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
  title={{eFFT}: An Event-based Method for the Efficient Computation of Exact {Fourier} Transforms},
  year={2024},
  volume={0},
  number={0},
  pages={0000-0000},
  doi={10.1109/TPAMI.2024.3422209}
}

📝 License

Distributed under the GPLv3 License. See LICENSE for more information.

📬 Contact

Raul Tapia - rault.nosp@m.apia.nosp@m.@us.e.nosp@m.s