PareCO: Pareto-aware Channel Optimization for Slimmable Neural Networks

Slimmable neural networks have been proposed recently for resource-constrained settings such as mobile devices as they provide a flexible trade-off front between prediction error and computational cost (such as the number of floating-point operations or FLOPs) with the same storage cost as a single model. However, current slimmable neural networks use a single width-multiplier for all the layers to arrive at sub-networks with different performance profiles, which neglects that different layers affect the network's prediction accuracy differently and have different FLOP requirements. We formulate the problem of optimizing slimmable networks from a multi-objective optimization lens, which leads to a novel algorithm for optimizing both the shared weights and the width-multipliers for the sub-networks. While slimmable neural networks introduce the possibility of only maintaining a single model instead of many, our results make it more realistic to do so by improving their performance.