Efficient On-Device Machine Learning with a Biologically-Plausible Forward-Only Algorithm

The training of the state-of-the-art Deep Neural Networks (DNNs) consumes massive amounts of energy, while the human brain learns new tasks with remarkable efficiency. Currently, the training of DNNs relies almost exclusively on Backpropagation (BP). However, BP faces criticism due to its biologically implausible nature, underscoring the significant disparity in performance and energy efficiency between DNNs and the human brain. Forward-only algorithms are proposed to be the biologically plausible alternatives to BP, to better mimic the learning process of the human brain and enhance energy efficiency. In this paper, we propose a biologically-plausible forward-only algorithm (Bio-FO), not only targeting the biological-implausibility issues associated with BP, but also outperforming the state-of-the-art forward-only algorithms. We extensively evaluate our proposed Bio-FO against other forward-only algorithms and demonstrate its performance across diverse datasets, including two real-world medical applications on wearable devices with limited resources and relatively large-scale datasets such as mini-ImageNet. At the same time, we implement our proposed on-device learning algorithm on the NVIDIA Jetson Nano and demonstrate its efficiency compared to other state-of-the-art forward-only algorithms. The code is available at https://github.com/whubaichuan/Bio-FO.