Abstract: The invention relates to a method for quantizing a deep neural network including several layers, previously trained during a training phase determining for each layer a set of weights. The method includes a phase of quantizing the deep neural network including determining a disruption limit value of at least one weight of the weight set of the layer, beyond which the output of the deep neural network is erroneous, determining, for a target inference precision of the neural network, and from the disruption limit value, an adjustment limit value of at least one weight of the set of weights, and decreasing an arithmetic precision of at least one weight of the set of weights as a function of the adjustment limit value. The invention also relates to a computer program, a device implementing such a method, and a deep neural network obtained by such a method.

Abstract: A method for simulating the behavior of a system (20) composed by a subsystem (10) in a physical environment (21), by a set of modules, each simulating a portion of said system, wherein at least one portion (11) of said subsystem (10) is simulated by a module implementing a multilayer neural network, the other modules implementing simulations based on behavioral physical laws; said method comprising the assessment (S2) of the results of the simulation to automatically decide (S3) on a strategy to trigger updates (S4) of parameters associated to said multilayer neural network.