Deep Neural Networks (DNNs) are very popular because of their high performance in
various cognitive tasks in Machine Learning (ML). Recent advancements in DNNs have …

Approximate computing is an emerging paradigm that, by relaxing the requirement for full
accuracy, offers benefits in terms of design area and power consumption. This paradigm is …

Libraries of approximate circuits are composed of fully characterized digital circuits that can
be used as building blocks of energy-efficient implementations of hardware accelerators …

Current state-of-the-art employs approximate multipliers to address the highly increased
power demands of deep neural network (DNN) accelerators. However, evaluating the …

Automated neural architecture search (NAS) methods are now employed to routinely deliver
high-quality neural network architectures for various challenging data sets and reduce the …

In this work, we introduce a control variate approximation technique for low error
approximate Deep Neural Network (DNN) accelerators. The control variate technique is …

Recent Deep Neural Networks (DNNs) manage to deliver superhuman accuracy levels on
many AI tasks. DNN accelerators are becoming integral components of modern systems-on …

Edge training of deep neural networks (DNNs) is a desirable goal for continuous learning;
however, it is hindered by the enormous computational power required by training …

There is a growing interest in automated neural architecture search (NAS) methods. They
are employed to routinely deliver high-quality neural network architectures for various …

Graph Neural Networks (GNNs) are one of the best-performing models for processing graph
data. They are known to have considerable computational complexity, despite the smaller …