# FFT-Based Deep Learning Deployment in Embedded Systems

arXiv: https://arxiv.org/abs/1712.04910 · HTML: https://mlsanity.com/paper/1712.04910/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Dec 2017.

## Built on by (most cited first, 6 of 6)

- [FNet: Mixing Tokens with Fourier Transforms](https://mlsanity.com/paper/2105.03824/) (2021)
- [Winograd Convolution for Deep Neural Networks: Efficient Point Selection](https://mlsanity.com/paper/2201.10369/) (2022)
- [Spectral-DP: Differentially Private Deep Learning through Spectral Perturbation and Filtering](https://mlsanity.com/paper/2307.13231/) (2023)
- [Hardware-Efficient Photonic Tensor Core: Accelerating Deep Neural Networks with Structured Compression](https://mlsanity.com/paper/2502.01670/) (2025)
- [Training with Differential Privacy: A Gradient-Preserving Noise Reduction Approach with Provable Security](https://mlsanity.com/paper/2409.11663/) (2024)
- [TriADA: Massively Parallel Trilinear Matrix-by-Tensor Multiply-Add Algorithm and Device Architecture for the Acceleration of 3D Discrete Transformations](https://mlsanity.com/paper/2506.22818/) (2025)

## Abstract (arXiv)

Deep learning has delivered its powerfulness in many application domains, especially in image and speech recognition. As the backbone of deep learning, deep neural networks (DNNs) consist of multiple layers of various types with hundreds to thousands of neurons. Embedded platforms are now becoming essential for deep learning deployment due to their portability, versatility, and energy efficiency. The large model size of DNNs, while providing excellent accuracy, also burdens the embedded platforms with intensive computation and storage. Researchers have investigated on reducing DNN model size with negligible accuracy loss. This work proposes a Fast Fourier Transform (FFT)-based DNN training and inference model suitable for embedded platforms with reduced asymptotic complexity of both computation and storage, making our approach distinguished from existing approaches. We develop the training and inference algorithms based on FFT as the computing kernel and deploy the FFT-based inference model on embedded platforms achieving extraordinary processing speed.