Data science projects represent a greater challenge than software engineering for
organizations pursuing their adoption. The diverse stakeholders involved emphasize the …

The main challenges are discussed together with the lessons learned from past and
ongoing research along the development cycle of machine learning systems. This will be …

The increasing paradigm shift towards i ntermittent computing has made it possible to
intermittently execute d eep neural network (DNN) inference on edge devices powered by …

As deep learning technology paves its way, real-world applications that make use of it
become popular these days. Edge computing architecture is one of the service architectures …

Assembly systems must provide maximum flexibility qualified by organization and
technology to offer cost-compliant performance features to differentiate themselves from …

The main challenges along with lessons learned from ongoing research in the application of
machine learning systems in practice are discussed, taking into account aspects of …

Deep neural network (DNN) implementations are typically characterized by huge datasets
and concurrent computation, resulting in a demand for high memory bandwidth due to …

Keyword spotting (KWS) utilities have become increasingly popular on a wide range of
mobile and home devices, representing a prolific application field for Convolutional Neural …

The automated architecture search methodology for neural networks is known as Neural
Architecture Search (NAS). In recent times, Convolutional Neural Networks (CNNs) …

The challenges involved in executing neural networks (NNs) at the edge include providing
diversity, flexibility, and sustainability. That implies, for instance, supporting evolving …