Recently, due to the availability of big data and the rapid growth of computing power,
artificial intelligence (AI) has regained tremendous attention and investment. Machine …

Deep Neural Network (DNN) has gained unprecedented performance due to its automated
feature extraction capability. This high order performance leads to significant incorporation …

The self-attention mechanism is rapidly emerging as one of the most important key primitives
in neural networks (NNs) for its ability to identify the relations within input entities. The self …

The nonuniform quantization strategy for compressing neural networks usually achieves
better performance than its counterpart, ie, uniform strategy, due to its superior …

Transformer Neural Networks have demonstrated leading performance in many applications
spanning over language understanding, image processing, and generative modeling …

Deep Neural Networks (DNNs) have gained unprecedented popularity due to their high-
order performance and automated feature extraction capability. This has encouraged …

Over the last decade, machine learning (ML) and deep learning (DL) algorithms have
significantly evolved and been employed in diverse applications, such as computer vision …

In recent years, the landscape of computing paradigms has witnessed a gradual yet
remarkable shift from monolithic computing to distributed and decentralized paradigms such …

Advances in deep neural networks (DNNs) and the availability of massive real-world data
have enabled superhuman levels of accuracy on many AI tasks and ushered the explosive …

An increasing number of software applications incorporate runtime Deep Neural Networks
(DNNs) to process sensor data and return inference results to humans. Effective deployment …