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.

Microwatt power hardware implementation of machine learning algorithms on MSP430 microcontrollers Defining characteristics of Machine learning are its ability to classify big data sets and to learn complex tasks as well as its required high computational power. In this paper we present a hardware implementation of widely used machine learning algorithms on an ultralow power microcontroller (MSP430, Texas Instruments) to detect epileptic seizures in recorded brain signals. Later, these detection algorithms are integrated in an implantable closedloop neurostimulator. By outsourcing mathematical operations from the microcontrolleru0027s CPU to its integrated hardware modules a power consumption of  82 mu mathrmW  for a decision tree ensemble and  802 mu mathrmW  for a convolutional neural network is achieved.