Ghasemi, Mohammad HosseinLucía, OscarLucia, Sergio2020-09-112020-09-112020-02-28https://depositonce.tu-berlin.de/handle/11303/11649http://dx.doi.org/10.14279/depositonce-10537With the rapid advancements of the internet of things, systems including sensing, communication, and computation become ubiquitous. The systems that are built with these technologies are increasingly complex and therefore require more automation and intelligent decision-making, while often including contact with humans. It is thus critical that such interactions run smoothly in real time, and that the automation strategies do not introduce important delays, usually not larger than 100 milliseconds, as the blink of a human eye. Pushing the deployment of the algorithms on embedded devices closer to where data is collected to avoid delays is one of the main motivations of edge computing. Further advantages of edge computing include improved reliability and data privacy management. This work showcases the possibilities of different embedded platforms that are often used as edge computing nodes: embedded microcontrollers, embedded microprocessors, FPGAs and embedded GPUs. The embedded solutions are compared with respect to their cost, complexity, energy consumption and computing speed establishing valuable guidelines for designers of complex systems that need to make use of edge computing. Furthermore, this paper shows the possibilities of hardware-agnostic programming using OpenCL, illustrating the price to pay in efficiency when software can be easily deployed on different hardware platforms.en004 Datenverarbeitung; InformatikInternet of Thingsedge computingFPGAsystem on chipneural networkArticle2169-3536