Neural Architecture Search (NAS) methods have been growing in popularity. These
techniques have been fundamental to automate and speed up the time consuming and error …

State-of-the-art deep learning models have achieved significant performance levels on
various benchmarks. However, the excellent performance comes at a cost of inefficient …

Convolutional neural networks (CNNs) are deep learning architectures used for image
classification that have been improved in recent years to increase their accuracies and …

Recent advances in deep learning allow on-demand reduction of model complexity, without
a need for re-training, thus enabling a dynamic trade-off between the inference accuracy …

Neural Networks (NN) have become a leading force in today's digital landscape. Inspired by
the human brain, their intricate design allows them to recognize patterns, make informed …

Deep neural networks have demonstrated remarkable efficacy across a wide range of tasks,
yet they face a significant limitation in their ability to adapt to distributional shifts. In contrast …

It is widely anticipated that inference models based on Deep Neural Networks (DNN) will be
actively employed in many edge platforms due to several compelling reasons. Firstly, DNNs …