MIRAMOND , Benoit

A simulator for hardware Spiking Neural Networks have been developed, in order to evaluate different implementation architectures in terms of processing latency, energy consumption, and chip surface. This simulator integrates different types of architectures, memory units distribution and memory technologies, in order to find which...

présentation invitée sans publication associée

La diffusion massive de l'IA repose essentiellement aujourd'hui sur l'utilisation de ressources de calcul dans le cloud, masquant ainsi l'incroyable consommation énergétique de ces algorithmes. L'ouverture de l'IA vers des contextes plus embarqués (AI at the Edge) révèle le défi qui attend ses acteurs et ouvre par la même occasion un nouveau...

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....

présentation invitée sans publication

International audience

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...

article dans Nice Matin page Santé

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...

International audience

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...

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...

Convolutional neural networks (CNNs) are now the de facto solution for computer vision problems thanks to their impressive results and ease of learning. These networks are composed of layers of connected units called artificial neurons, loosely modeling the neurons in a biological brain. However, their implementation on conventional hardware...

Local plasticity mechanisms enable our brains to self-organize, both in structure and function, in order to adapt to the environment. This unique property is the inspiration for this study: we propose a brain-inspired computational model for self-organization, then discuss its impact on theclassification accuracy and the energy-efficiency of an...

In the recent years, Spiking Neural Networks have gain much attention from the research community. They can now be trained using the powerful gradient descent and have drifted from the neuroscience to the Machine Learning community. Anabundant literature shows that they can perform well on classical Artificial Intelligence tasks such as image...

Few-shot classification is a challenge in machine learning where the goal is to train a classifier using a very limited number of labeled examples. This scenario is likely to occur frequently in real life, for example when data acquisition or labeling is expensive. In this work,we consider the problem of post-labeled few-shot unsupervised...

The recent migration from single-core to multi-core platforms in the automotive domain reveals great challenges for the legacy embedded software design flow. First of all, software designers need new methods to fill the gap between application description and tasks deployment. Secondly, the use of multiple cores has also to remain compatible...

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...

Cortical plasticity is one of the main features that enable our capability to learn and adapt in our environment. Indeed, the cerebral cortex has the ability to self-organize itself through two distinct forms of plasticity: the structural plasticity that creates (sprouting) or cuts (pruning) synaptic connections between neurons, and the...

Our brain-inspired computing approach attempts to simultaneously reconsider AI and von Neumann's architecture. Both are formidable tools responsible for digital and societal revolutions, but also intellectual bottlenecks linked to the ever-present desire to ensure the system is under control. The brain remains our only reference in terms of...