Accelerating Neural ODEs Using Model Order Reduction

TL;DR

This paper introduces a novel approach to accelerate Neural ODEs by applying model order reduction techniques, enabling faster inference while maintaining accuracy, especially useful for resource-limited environments.

Contribution

The authors develop a new compression method for Neural ODEs using model order reduction, improving inference speed without significant loss of accuracy.

Findings

Achieved faster Neural ODE inference with minimal accuracy loss.

Compared favorably against pruning and SVD truncation methods.

Effective in both image and time-series classification tasks.

Abstract

Embedding nonlinear dynamical systems into artificial neural networks is a powerful new formalism for machine learning. By parameterizing ordinary differential equations (ODEs) as neural network layers, these Neural ODEs are memory-efficient to train, process time-series naturally and incorporate knowledge of physical systems into deep learning models. However, the practical applications of Neural ODEs are limited due to long inference times, because the outputs of the embedded ODE layers are computed numerically with differential equation solvers that can be computationally demanding. Here we show that mathematical model order reduction methods can be used for compressing and accelerating Neural ODEs by accurately simulating the continuous nonlinear dynamics in low-dimensional subspaces. We implement our novel compression method by developing Neural ODEs that integrate the necessary subspace-projection and interpolation operations as layers of the neural network. We validate our approach by comparing it to neuron pruning and SVD-based weight truncation methods from the literature in image and time-series classification tasks. The methods are evaluated by acceleration versus accuracy when adjusting the level of compression. On this spectrum, we achieve a favourable balance over existing methods by using model order reduction when compressing a convolutional Neural ODE. In compressing a recurrent Neural ODE, SVD-based weight truncation yields good performance. Based on our results, our integration of model order reduction with Neural ODEs can facilitate efficient, dynamical system-driven deep learning in resource-constrained applications.