LightFF: Lightweight Inference for Forward-Forward Algorithm

Abstract

The human brain performs tasks with an outstanding energy efficiency, i.e., with approximately 20 Watts. The state-of-the-art Artificial/Deep Neural Networks (ANN/DNN), on the other hand, have recently been shown to consume massive amounts of energy. The training of these ANNs/DNNs is done almost exclusively based on the back-propagation algorithm, which is known to be biologically implausible. This has led to a new generation of forward-only techniques, including the Forward-Forward algorithm. In this paper, we propose a lightweight inference scheme specifically designed for DNNs trained using the Forward-Forward algorithm. We have evaluated our proposed lightweight inference scheme in the case of the MNIST and CIFAR datasets, as well as two real-world applications, namely, epileptic seizure detection and cardiac arrhythmia classification using wearable technologies, where complexity overheads/energy consumption is a major constraint, and demonstrate its relevance. Our code is available at this https URL. https://github.com/AminAminifar/LightFF

BibTeX

@inproceedings{aminifar2024lightff,
  title={LightFF: Lightweight Inference for Forward-Forward Algorithm},
  author={Aminifar, Amin and Huang, Baichuan and Fahliani, Azra Abtahi and Aminifar, Amir},
  booktitle={27th European Conference on Artificial Intelligence, ECAI-2024},
  pages={1728--1735},
  year={2024},
  organization={IOS Press}
}