Energy Efficient Implementation of Machine Learning Algorithms on Hardware Platforms
Mario Osta, Mohamad Alameh, Hamoud Younes, Alì Ibrahim, Maurizio Valle · 2019
Machine and deep learning algorithms are currently employed for many applications such as computer vision, speech recognition and portable/wearable 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 (ML/DL) 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 ML/DL algorithm on embedded hardware platforms.