ABDERRAHMANE , Nassim

Nowadays, Artificial Intelligence (AI) is a widespread concept applied to many fields such as transportation, medicine and autonomous vehicles. The main AI algorithms are artificial neural networks, which can be divided into two families: Spiking Neural Networks (SNNs), which are bio-inspired models resulting from neuroscience, and Analog...

Recent literature considers that Spiking Neural Networks are now a serious alternative of Formal Neural Networksfor embedded artificial intelligence. The changes in the information coding and the elementary neural computation makethem more efficient than FNNs in terms of power consumption and chip surface occupation. However, these results are...

Artificial neural networks are experiencing an exclusive interest due to the unprecedented computing power capabilities the computers reached and the explosion of open data. The recent results of deep neural networks on image classification has given neural networks the leading role in machine learning algorithms and artificial intelligence...

Inspired from the brain, neuromorphic computing would be the right alternative to traditional Von-Neumann architecture computing that knows its end of growth as predicted by Moore's law. In this paper, we explore bio-inspired neural networks as an AI-accelerator for embedded systems. To do so, we first map neural networks from formal to spiking...

Artificial neural networks are experiencing today an unprecedented interest thanks to two main changes: the explosion of open data that is necessary for their training, and the increasing computing power of today's computers that makes the training part possible in a reasonable time. The recent results of deep neural networks on image...

Machine learning is yielding unprecedented interest in research and industry, due to recent success in many applied contexts such as image classification and object recognition. However, the deployment of these systems requires huge computing capabilities, thus making them unsuitable for embedded systems. To deal with this limitation, many...

In this paper, we present SPLEAT, a SPiking Low-power Event-based ArchiTecture for the hardware deployment of Spiking Neural Networks (SNN). SPLEAT is designed as a configurable architecture that allows integrating several hardware modules representing different neural layers and, therefore, gives the ability to deploy a wide range of deep...

Spiking neural networks are expected to bring high resources, power and energy efficiency to machine learning hardware implementations. In this regard, they could facilitate the integration of Artificial Intelligence in highly constrained embedded systems, such as image classification in drones or satellites. If their logic resource efficiency...

Artificial neural networks (ANNs) incur huge costs in terms of processing power, memory performance, and energy consumption, where in comparison an average human brain operates within a power budget of nearly 20 W. Brain-inspired computing such as Spiking Neural Networks (SNNs) are thus expected to improve efficiency to an unprecedented extent....