Dynamic Inference Techniques in Convolutional Neural Networks: A Review

Abstract

The demand for more accurate Convolutional Neural Networks (CNNs) has led to increasingly deep and computationally expensive models. These architectures face limiting deployment and resource constraints when implemented. Static CNN models treat all input samples with identical computational density. These models are inefficient given the variability of real-world data. Dynamic inference solutions focus on this challenge by presenting computational graphs on a per-input basis. This allows considerable optimization of the efficiency–accuracy trade-off. This review offers a structured synthesis of dynamic inference solutions in CNNs, distributed over three architectural dimensions: depth, width, and spatial resolution. For each aspect, we present representative models, alongside their training strategies, and examine their practical constraints and performance trade-offs. We further present multi-dimensional hybrid architectures based on combining these aspects for accumulated efficiency gains. By critically analyzing each approach and their challenges—including hardware limitations, training instability, and constrained deployment scope—this paper offers a roadmap for future work in optimized deep learning modelling.