Abstract: The present disclosure relates to a spike event decision-making device, method, chip, and electronic device to eliminate inherent delays of an output spike event of the neuromorphic chip when reading decisions. The spike event decision-making device includes a first counting module configured to count a number of input spike events of the spiking neural network, a second counting module configured to count some or all of the output spike events of the spiking neural network; and a decision-making module configured to generate a decision-making result according to numbers of spike events fired by neurons in an output layer of the spiking neural network when the number counted by the first counting module reaches a first predetermined value, or when the total count counted by the second counting module reaches a second predetermined value.

Abstract: A detection device is provided in the disclosure. The device uses unsupervised or self-supervised neural networks to learn nominal conditions of a target system, such as a device or a machine. The trained neural networks can reproduce sensory signals of the target system as a neural-network-reconstructed version of the sensory signals in the nominal conditions of a target system. The equipment anomaly detection device may analyze and predict operation conditions of the target system based on the neural-network-reconstructed version exceeding a certain level. When signal difference between the sensory signals and the neural-network-reconstructed version exceeds a certain level, the equipment anomaly detection device may issue an alert signal to reflect abnormal operation conditions of the target system.

Abstract: The invention relates to an event-driven spiking neural network system (100) and a method for detecting a physiological condition of a person based on a detected physiological signal of the person, the system comprising at least the following components: At least one sensor (111-1) configured and arranged to detect a physiological signal and to convert the physiological signal in a sensor signal (112) indicative of the physiological signal, A signal conversion module (120) configured and arranged to receive the sensor signal (112) from the at least one sensor (111-1) and to convert the sensor signal (112) in at least one time series of discrete events, An artificial neuron population (140) comprising a plurality of artificial event-driven spiking neurons (131-1, 131-N) arranged in an event-driven spiking neural network, wherein the neuron population (140) is configured and arranged to receive events, wherein the neuron population (140) is arranged to recognize the physiological condition of the person based o

Abstract: The invention relates to an event-driven spiking convolutional neural network, comprising a plurality of layers, wherein each layer comprises a kernel module (110) configured to store and to process in an event-driven fashion kernel values of at least one convolution kernel (410); a neuron module (120) configured to store and to update in an event-driven fashion neuron states of neurons of the spiking neural network (1), and to output spike events (150) generated from updated neurons (420); a memory mapper (130) configured to determine neurons (420) to which an incoming spike event (140) from a source layer projects by means of a convolution with at least one convolution kernel (410) and wherein neuron states of said determined neurons (420) are to be updated with applicable kernel values of the at least one convolution kernel (410), wherein the memory mapper (130) is configured to process incoming spike events in an event-driven fashion.