CASTAGNETTI , Andrea

Spiking neural networks are considered as the third generation of Artificial Neural Networks. SNNs perform computation using neurons and synapses that communicate using binary and asynchronous signals known as spikes. They have attracted significant research interest over the last years since their computing paradigm allows theoretically sparse...

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In recent years, Deep Convolutional Neural Networks (DCNNs) have outreached the performance of classical algorithms for image restoration tasks. However most of these methods are not suited for computational efficiency.In this work we investigate Spiking Neural Networks (SNNs) for the specific and uncovered case of image denoising, with the...

Spiking neural networks are considered as the third generation of Artificial Neural Networks. SNNs perform computation using neurons and synapses that communicate using binary and asynchronous signals known as spikes. They have attracted significant research interest over the last years since their computing paradigm allows theoretically sparse...

In this paper we propose an analytical Lithium Polymer (LiPo) rechargeable battery modeling approach to accurately predict battery SoC during simulations. This approach is based on a data-driven method for modeling the aging phenomenon present in rechargeable batteries. The proposed battery model is integrated into a system-level modeling...

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Road safety applications based on embedded systems are becoming increasingly reliable for the assistance of vehicle drivers. Cars start to be equipped with applications such as the detection of falling asleep at the wheel. The Ellcie-Healthy start-up is developing smart connected glasses, a wearable device designed for e-Health and driver...

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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,...

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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