Early-Exit with Class Exclusion for Efficient Inference of Neural Networks

TL;DR

This paper introduces a class-based early-exit method for neural network inference that reduces computational cost by excluding irrelevant classes at intermediate layers, enabling faster and more resource-efficient predictions.

Contribution

The paper proposes a novel class exclusion early-exit strategy that leverages learned features to dynamically reduce classes during inference, improving efficiency.

Findings

Significant reduction in inference computational cost.

Effective exclusion of irrelevant classes at intermediate layers.

Maintains high accuracy with reduced computation.

Abstract

Deep neural networks (DNNs) have been successfully applied in various fields. In DNNs, a large number of multiply-accumulate (MAC) operations are required to be performed, posing critical challenges in applying them in resource-constrained platforms, e.g., edge devices. To address this challenge, in this paper, we propose a class-based early-exit for dynamic inference. Instead of pushing DNNs to make a dynamic decision at intermediate layers, we take advantage of the learned features in these layers to exclude as many irrelevant classes as possible, so that later layers only have to determine the target class among the remaining classes. When only one class remains at a layer, this class is the corresponding classification result. Experimental results demonstrate the computational cost of DNNs in inference can be reduced significantly with the proposed early-exit technique. The codes can be found at https://github.com/HWAI-TUDa/EarlyClassExclusion.