Artificial Vision on Edge IoT Devices: A Practical Case for 3D Data Classification Nowadays, with the huge advance of sensor technology and the increase of the amount of data generated by them, techniques have to be developed to be able to process all this amount of information in realtime applications on edge devices, close to where data is being generated. If all that information has to be sent to the cloud to be processed, it has certain disadvantages in terms of latency, bandwidth, privacy and reliability, compared to locally processing it on the edge. In this paper, the implementation of deep learning algorithms in low power and limited resources devices in an Internet of Things scenario is studied. In order to work in realtime applications, the influence of different low power consumption deep learning hardware accelerators is studied. Finally, a practical case for smart farming is shown with comparative results in terms of power consumption and performance when running the same artificial vision algorithm on different devices.

Energy Efficient Implementation of Machine Learning Algorithms on Hardware Platforms Machine and deep learning algorithms are currently employed for many applications such as computer vision, speech recognition and portablewearable electronics. Regrettably, machine learning algorithms have high complexity adding more challenges for the implementation of such algorithms on embedded hardware platforms. This paper aims to present an overview about state of the art techniques enabling efficient implementation of Machine and Deep learning (MLDL) algorithms aiming to improve the energy efficiency. An assessment of the algorithms suitable for embedded implementation is provided, presenting some hardware platforms supporting artificial intelligent systems. On the other hand, we have exploited the choice of implementing MLDL algorithm on embedded hardware platforms.