NOVAC , Pierre-Emmanuel

L'objectif de ce stage est d'introduire aux problèmes de traitement designal en temps réel pour les environnements embarqués.Il s'agira de concevoir des noyaux de convolutions en VHDL etd'analyser leur coût matériel pour différents jeux de paramètres(taille des échantillons, taille du noyau, valeur descoéfficients...).Le stage sera donc découpé...

This report is about my internship at the LEAT laboratory, Sophia Antipolis, France. This is my final internshipas a part of my engineering degree in Computer Science, from March 4, 2019 to August 30, 2019. Artificial Neural Networks are becoming more and more popular to achieve all sort of tasks such as classification of data. Neuromorphic...

With the growth of the senior population, elderly care becomes an important topic in the society.One aspect of elderly care is fall prevention, which is still a challenging task depending on the subject's health condition. In this context, artificial intelligence can be leveraged to notifyabout an increased risk. To achieve this goal, a...

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Human activity recognition can help in elderly care by monitoring the physical activities of a subject and identifying a degradation in physical abilities. Vision-based approaches require setting up cameras in the environment, while most body-worn sensor approaches can be a burdenon the elderly due to the need of wearing additional devices....

Embedding Artificial Intelligence onto low-power devices is a challenging task that has been partly overcome with recent advances in machine learning and hardware design. Presently, deep neural networks can be deployed on embedded targets to perform different tasks such as speech recognition, object detection or Human Activity Recognition....

Bringing artificial intelligence to embedded devices has become a central research topic in many scientific domains (environment, agriculture, sociology, health...). For Human Activity Recognition, Artificial Neural Networks (ANNs) have shown their capability to provide better performance compared to other machine learning methods. However,...

Efficient dynamic allocation of computation resources can enable significant improvements in terms of performance and /or power consumption of hardware architectures.In this paper, we propose three bio-inspired mechanisms for self-organizing cellular hardware architecture built on top of the Cellular Self-Organizing Map (CSOM) vector...

Spiking Neural Networks are a type of neural networks where neurons communicate using only spikes. They are often presented as a low-power alternative to classical neural networks, but few works have proven these claims to be true.In this work, we present a metric to estimate the energy consumption of SNNs independently of a specific hardware....

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