ABSTRACT
Machine Learning is nowadays embedded in several computing devices, consumer electronics and cyber-physical systems. Smart sensors are deployed everywhere, in applications such as wearables and perceptual computing devices, and intelligent algorithms power our connected world. Near-sensor computation and near-sensor intelligence are starting to emerge as necessities, in order to continue supporting the paradigm shift of our connected world. Artificial Intelligence is currently facilitated using machine learning algorithms such as Deep Neural Networks which demands high computation and memory requirements. Traditional Von Neumann architectures are no longer sufficient and suitable, primarily because of limitations in both performance and energy efficiency caused especially by large amounts of data movement.
