Recent advances in computing allows researchers to propose the automation of hydroponic
systems to boost efficiency and reduce manpower demands, hence increasing agricultural …

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 …

Deploying Machine learning (ML) on milliwatt-scale edge devices (tinyML) is gaining
popularity due to recent breakthroughs in ML and Internet of Things (IoT). Most tinyML …

With concerns about data privacy growing in a connected world, cryptography researchers
have focused on fully homomorphic encryption (FHE) for promising machine learning as a …

Intermittent systems become popular to be considered as the solutions of various application
domains, thanks to the maturation of energy harvesting technology. Environmental …

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 …

With concerns about data privacy growing in a connected world, cryptography researchers
have focused on fully homomorphic encryption (FHE) for promising machine learning as a …