… on mobile and embedded platforms, … resource management in heterogeneous multi-core
systems and show how they can be applied to optimise the performance of machine learning …

… Building a machine learning model may take a few steps, but we are only going to focus
on the two most important ones: training/learning and validation/simulation phases. The …

… maintain a sufficient level of service without overloading the processor as well as providing
an energy savings of almost 20% as compared to the native resource management in Linux. …

… - and DL-based resource management mechanisms in cellular wireless … resource management
in cellular IoT and low-power IoT networks, review the traditional resource management …

… faced in embedded machine learning and review … deep learning models within
resource-constrained environments. We present diverse application areas of embedded machine …

… 2.1 Setup To acquire a representative data-set for multicore real-time embedded systems, …
resource sharing contention between tasks when running parallel in a multicore embedded …

… , we propose a machine learning-based method for supporting resource management of IoT
… , ultrasonic, accelerometer, through different embedded system interfaces such as general …

… of-the-art research in the embedded machine learning area for different applications, so that
… well predict the general trajectory of embedded machine learning advances. The method of …

… , embedded machine learning must not be seen as a replacement to cloud-based AI. In most
cases, embedded machine learning … -time control operations and privacy management for …

… intelligence embedded in this wearable unit can be. This study shows the interesting
characteristics brought by these state-of-the-art paradigms, concluding that a wide variety of novel …