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 …

The challenging deployment of compute-intensive applications from domains such as
Artificial Intelligence (AI) and Digital Signal Processing (DSP), forces the community of …

To enable efficient deployment of convolutional neural networks (CNNs) on embedded
platforms for different computer vision applications, several convolution variants have been …

Approximate computing, frequently used in error tolerant applications, aims to achieve
higher circuit performances by allowing the possibility of inaccurate results, rather than …

Approximate computing along with quantized low-precision computing has gained
significant interest in today's neural network (NN) implementation. This paper proposes a …

Recent advancements in hardware and software systems have been driven by the
deployment of emerging smart health and mobility applications. These developments have …

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 …

Approximate arithmetic operators, such as adders and multipliers, are increasingly used to
satisfy the energy and performance requirements of resource-constrained embedded …

This paper presents the Weight-aware Approximate Multiplication Architecture (W-AMA) for
Deep Neural Networks (DNNs). Considering the Gaussian-like weight distribution, it deploys …

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 …