Commissariat &#xc0; L'&#xc9;nergie Atomique Et Aux &#xc9;nergies Alternatives

Centre National De La Recherche Scientifique

Thales

Damien Querlioz

Olivier Bichler

Christian Gamrat

Julie Grollier

Vincent Cros

Manan Suri

Barbara De Salvo

Pierre Bessiere

Jacques Droulez

Daniele Pinna

A device for generating a random electric signal, including an input duct, an output duct, a generator of magnetic particles generating magnetic particles in the input duct, a diffusion chamber connected to the input duct and the output duct, wherein the diffusion chamber is designed to diffuse the generated magnetic particles, a displacement unit for displacement of the generated magnetic particles towards the diffusion chamber, and a converter that is designed to generate an electrical signal proportional to a characteristic, wherein the characteristic is the particle density in the diffusion chamber or the passage of magnetic particles at a predetermined location of an output duct connected to the diffusion chamber.

Device for generating a random electrical signal and associated architecture

A neuromorphic system comprises a set of at least one input neuron, a set of at least one output neuron and a synaptic network formed from a set of at least one variable-resistance memristive component, said synaptic network connecting at least one input neuron to at least one output neuron, the resistance of the at least one memristive component being adjusted by delivering to the synaptic network write pulses generated by the at least one input neuron, and return pulses generated by the at least one output neuron, the characteristics of the write and return pulses being deduced from the intrinsic characteristics of the at least one memristive component so that the combination of a write pulse and a return pulse in the at least one memristive component results in a modification of its resistance according to a learning rule chosen beforehand.

Neuromorphic system exploiting the intrinsic characteristics of memory cells

An unsupervised learning method is provided implemented in an artificial neural network based on memristive devices. It consists notably in producing an increase in the conductance of a synapse when there is temporal overlap between a pre-synaptic pulse and a post-synaptic pulse and in decreasing its conductance on receipt of a post-synaptic pulse when there is no temporal overlap with a pre-synaptic pulse.

Method for non-supervised learning in an artificial neural network based on memristive nanodevices, and artificial neural network implementing said method

Growing community of inventors

Sceaux, France

Damien Querlioz