Applying the Forward-Forward Algorithm To Event-Based Sensing

Abstract

The forward-forward algorithm is a recently introduced method that can be used to train artificial neural networks. Currently, the canonical approach to training artificial neural networks is using (batched) stochastic gradient descent with backpropagation to minimize a carefully designed cost function. As successful as it may be in realizing artificial neural networks on modern digital computers, the backpropagation algorithm is biologically implausible. Unlike backpropagation, the forward-forward algorithm does not propagate the partial derivatives of the cost function backwards throughout the whole network and does not require storing or re-computing layer activations. As a result, the forward-forward algorithm is closer to being biologically plausible. As mentioned in Hinton's original paper, the forward-forward algorithm is well-suited for low-power platforms, indicating the possibility of on-device/on-edge training. Coincidentally, event-based (neuromorphic) visual sensors, praised for their power efficiency and high dynamic range, are becoming increasingly commercially available. In this paper we apply the forward-forward algorithm to train relatively small network models to classify event-based visual inputs. We first implement the forward-forward algorithm to reproduce the results obtained with the MNIST dataset in the original paper. Next, we switch the input data to the event-based version of the MNIST dataset and propose novel modifications to the forward-forward training process to make it applicable to event-based visual inputs. Finally, we present a comparative performance analysis. All PyTorch scripts that generated the results in this paper are available here: https://github.com/SuleymanEmirAkin/EventBased_Forward-Forward. © 2024 Copyright held by the owner/author(s).