… -art embedded sensors, communication technologies, computing platforms and machine
learning … This review will concentrate on some of the most recently developed machine learning …

… this convergence of embedded systems and machine learning. … in embedded machine
learning and review different optimization techniques to enhance the execution of deep learning …

… of specialized accelerators for machine learning (ML) has … for enabling machine learning
into embedded devices at the … However, to realize innovative embedded systems for such …

… the implementation of embedded machine learning somewhat difficult. … of machine learning
on embedded systems divided by their specific device, application, specific machine learning …

… embedded platforms, at both design time and runtime. At design time, DNNs are deployed
on a variety of hardware platforms … and optimise machine learning workloads alongside other …

… and a plethora of new platforms are available on the market. Some of them either have
embedded GPUs or the possibility to be connected to external Machine Learning (ML) algorithm …

… system, the neural network will often be trained on a different processing platform than the
… the inference part of deep learning. The terms “deep learning” and “machine learning” in the …

… The machine that is learning must be able to create a general … Machine learning algorithms
can be implemented in available microcontrollers, which are used in various embedded …

… Furthermore, to cope with machine learning in a variety of embedded platforms, developers
and deployers must be familiar with a variety of different systems and tools, which is yet …

… 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 …