Mitigating Communication Costs in Neural Networks: The Role of Dendritic Nonlinearity
This research addresses communication overhead in neural network training and inference, offering insights for designing accelerators, though it appears incremental in confirming prior suggestions about dendritic benefits.
The study tackled the role of dendritic nonlinearities in neural networks, finding that they do not significantly enhance learning capacity but instead enable network capacity expansion while reducing communication costs through localized feature aggregation.
Our understanding of biological neuronal networks has profoundly influenced the development of artificial neural networks (ANNs). However, neurons utilized in ANNs differ considerably from their biological counterparts, primarily due to the absence of complex dendritic trees with local nonlinearities. Early studies have suggested that dendritic nonlinearities could substantially improve the learning capabilities of neural network models. In this study, we systematically examined the role of nonlinear dendrites within neural networks. Utilizing machine-learning methodologies, we assessed how dendritic nonlinearities influence neural network performance. Our findings demonstrate that dendritic nonlinearities do not substantially affect learning capacity; rather, their primary benefit lies in enabling network capacity expansion while minimizing communication costs through effective localized feature aggregation. This research provides critical insights with significant implications for designing future neural network accelerators aimed at reducing communication overhead during neural network training and inference.