While deep learning has shown superior performance in various intelligent tasks, it is still a
challenging problem to deploy sophisticated models on resource-limited edge devices. Filter …

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 …

The emergence of the Internet of things (IoT) brings a new paradigm of ubiquitous sensing
and computing. Yet as an increasing number of wireless IoT devices are deployed …

Internet-of-Things (IoT) devices are becoming both intelligent and green. On the one hand,
Deep Neural Network (DNN) compression techniques make it possible to run deep learning …

A fundamental problem of every intermittently-powered sensing system is that signals
acquired by these systems over a longer period in time are also intermittent. As a …

Implementing a neural network (NN) inference in a millimeter-scale system is challenging
due to limited energy and storage size. This article proposes an energy-aware adaptive NN …

Wireless sensor systems powered by batteries are widely used in a variety of applications.
For applications with space limitation, their size was reduced, limiting battery energy …

For many practical applications, a high computational cost of inference over deep network
architectures might be unacceptable. A small degradation in the overall inference accuracy …

Compared to traditional neural networks with a single exit, a multi-exit network has multiple
exits that allow for early output from intermediate layers of the model, thus bringing …

The ubiquity of executing machine learning tasks on embedded systems with constrained
resources has made efficient execution of neural networks on these systems under the CPU …