  Deep learning (DL) techniques are increasingly pervasive across various
domains, including wireless communication, where they extract insights from raw
radio signals. However, the computational demands of DL pose significant
challenges, particularly in distributed wireless networks like Cell-free
networks, where deploying DL models on edge devices becomes hard due to
heightened computational loads. These computational loads escalate with larger
input sizes, often correlating with improved model performance. To mitigate
this challenge, Early Exiting (EE) techniques have been introduced in DL,
primarily targeting the depth of the model. This approach enables models to
exit during inference based on specified criteria, leveraging entropy measures
at intermediate exits. Doing so makes less complex samples exit early, reducing
computational load and inference time. In our contribution, we propose a novel
width-wise exiting strategy for Convolutional Neural Network (CNN)-based
architectures. By selectively adjusting the input size, we aim to regulate
computational demands effectively. Our approach aims to decrease the average
computational load during inference while maintaining performance levels
comparable to conventional models. We specifically investigate Modulation
Classification, a well-established application of DL in wireless communication.
Our experimental results show substantial reductions in computational load,
with an average decrease of 28%, and particularly notable reductions of 65% in
high-SNR scenarios. Through this work, we present a practical solution for
reducing computational demands in deep learning applications, particularly
within the domain of wireless communication.
