DNNs are becoming less and less over-parametrised due to recent advances in efficient
model design, through careful hand-crafted or NAS-based methods. Relying on the fact that …

We present an approach to adaptively utilize deep neural networks in order to reduce the
evaluation time on new examples without loss of accuracy. Rather than attempting to …

Adaptive inference is a promising technique to improve the computational efficiency of deep
models at test time. In contrast to static models which use the same computation graph for all …

We present any-precision deep neural networks (DNNs), which are trained with a new
method that allows the learned DNNs to be flexible in numerical precision during inference …

State-of-the-art convolutional neural networks (CNNs) yield record-breaking predictive
performance, yet at the cost of high-energy-consumption inference, that prohibits their widely …

Mobile and embedded platforms are increasingly required to efficiently execute
computationally demanding DNNs across heterogeneous processing elements. At runtime …

The design of specialized architectures for accelerating the inference procedure of Deep
Neural Networks (DNNs) is a booming area of research nowadays. While first-generation …

To realize the promise of ubiquitous embedded deep network inference, it is essential to
seek limits of energy and area efficiency. To this end, low-precision networks offer …

Early exiting is an effective paradigm for improving the inference efficiency of deep networks.
By constructing classifiers with varying resource demands (the exits), such networks allow …

The problem of reducing processing time of large deep learning models is a fundamental
challenge in many real-world applications. Early exit methods strive towards this goal by …