47 citations · 55 across the 7 of their papers we have counts for
6 papers · 1 filter
A unified software/hardware scalable architecture for brain-inspired computing based on self-organizing neural models
Artem R. Muliukov, Laurent Rodriguez, Benoit Miramond +4
The field of artificial intelligence has significantly advanced over the past decades, inspired by discoveries from the fields of biology and neuroscience. The idea of this work is…
GPU-based Self-Organizing Maps for Post-Labeled Few-Shot Unsupervised Learning
Lyes Khacef, Vincent Gripon, Benoit Miramond
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…
Improving Self-Organizing Maps with Unsupervised Feature Extraction
Lyes Khacef, Laurent Rodriguez, Benoit Miramond
The Self-Organizing Map (SOM) is a brain-inspired neural model that is very promising for unsupervised learning, especially in embedded applications. However, it is unable to learn…
Brain-inspired self-organization with cellular neuromorphic computing for multimodal unsupervised learning
Lyes Khacef, Laurent Rodriguez, Benoit Miramond
Cortical plasticity is one of the main features that enable our ability to learn and adapt in our environment. Indeed, the cerebral cortex self-organizes itself through structural…
Design Space Exploration of Hardware Spiking Neurons for Embedded Artificial Intelligence
Nassim Abderrahmane, Edgar Lemaire, Benoît Miramond
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. Ho…
Neuromorphic hardware as a self-organizing computing system
Lyes Khacef, Bernard Girau, Nicolas Rougier +2
This paper presents the self-organized neuromorphic architecture named SOMA. The objective is to study neural-based self-organization in computing systems and to prove the feasibil…