This repository provides the reproducible code for all the reported results in the paper TinyFoA: Memory Efficient Forward-Only Algorithm for On-Device Learning.
The codes for TinyFoA on MNIST, CIFAR-10, CIFAR-100, and MIT-BIH datastes are provided. Taking MNIST as an example, the codes are shown as follows:
- MNIST-TinyFoA_FC:
python TinyFoA_FC.py - MNIST-TinyFoA_LC:
python TinyFoA_LC.py
the parameters
datasetneed to be changed accordingly.
The codes for BP and the state-of-the-art forward-only algorithms are provided, including DRTP[1], PEPITA[2], and FF[3] on MNIST, CIFAR-10, CIFAR-100, and MIT-BIH datastes.
Taking CIFAR-10 as an example, the codes are shown as follows:
- CIFAR-10-DRTP+BW+BA:
ppython Others/DRTP/main.py - CIFAR-10-PEPITA+BW+BA:
python Others/pepita.py - CIFAR-10-FF+BW+BA:
python Others/FF/main.py - CIFAR-10-BP(FC)+BW+BA+V:
python Others/BP_FC.py - CIFAR-10-BP(LC)+BW+BA+V:
python Others/BP_LC.py
the parameters
datasetneed to be changed accordingly. We acknowledge the following repositories DRTP, PEPITA and FF.
[1] Frenkel, Charlotte, Martin Lefebvre, and David Bol. "Learning without feedback: Fixed random learning signals allow for feedforward training of deep neural networks." Frontiers in neuroscience 15 (2021): 629892.
[2] Dellaferrera, Giorgia, and Gabriel Kreiman. "Error-driven input modulation: solving the credit assignment problem without a backward pass." International Conference on Machine Learning. PMLR, 2022.
[3] Hinton, Geoffrey. "The forward-forward algorithm: Some preliminary investigations." arXiv preprint arXiv:2212.13345 (2022).
@article{Huang_Aminifar_2025,
title={TinyFoA: Memory Efficient Forward-Only Algorithm for On-Device Learning},
volume={39},
url={https://ojs.aaai.org/index.php/AAAI/article/view/33910},
DOI={10.1609/aaai.v39i16.33910},
number={16},
journal={Proceedings of the AAAI Conference on Artificial Intelligence},
author={Huang, Baichuan and Aminifar, Amir},
year={2025},
month={Apr.},
pages={17377-17385}
}