Abstract:
Provided are an autonomous driving system and a correction learning method for autonomous driving. The autonomous driving system includes a sensor configured to collect and output data required for autonomous driving, a first processor configured to output autonomous driving data on the basis of data input from the sensor, a second processor configured to output a driving data adjustment value on the basis of differences between the data input from the sensor, the autonomous driving data input from the first processor, and driving data input from driving by a human driver, and a driving part configured to perform driving on the basis of the autonomous driving data output from the first processor and the driving data adjustment value output from the second processor.
Abstract:
Provided is a method of operating a neuron in a neuromorphic system. The method includes evaluating a membrane potential value at a corresponding time when receiving an input spike, time-modulating a synaptic weight of the membrane potential value and converting the time-modulated synaptic weight into a membrane potential value at a reference time, and generating an output spike when the membrane potential value at the reference time exceeds a certain threshold value. The membrane potential value at the reference time is represented by a floating point number including a predetermined bit of exponent and mantissa, and the floating point number includes time information. The method further includes accessing a memory and scanning a neural state variable when a timer is updated to “0” to update the neural state variable to an updated value at a reference time.
Abstract:
The present invention relates to a non-invasive health indicator monitoring system including a sensing module, an electric power storage module, and a circuit module to collect health indicator information by contacting with a subject. In addition, the present invention also relates to a method for monitoring health indicator continuously by using the health indicator monitoring system.
Abstract:
Provided is a neuromorphic system for synaptic learning in a spiking neural network (SNN)-based neuromorphic array structure. Control blocks including a post-synaptic neuron, which generates a post-neuron spike, are disposed on output lines of a synapse array to implement a spike timing dependent plasticity (STDP) operation such that synaptic learning can be stably implemented in an SNN neuromorphic array. Also, a lateral inhibition circuit may be added. When a post-neuron spike is generated by an STDP control block connected to any one output line, the lateral inhibition circuit inhibits STDP control blocks connected to other output lines from generating spikes. Accordingly, learning selectivity can be improved, and thus the performance of an STDP algorithm can be improved.
Abstract:
A neuromorphic device includes: a neuron block unit including a plurality of neurons; a synapse block unit including a plurality of synapses; and a topology block unit including a plurality of parallel Look-Up Table (LUT) modules including pre and post neuron elements configured with addresses of a presynaptic neuron and a postsynaptic neuron. Each of the plurality of neurons has an intrinsic address, each of the plurality of synapses has an intrinsic address. The parallel LUT module is partitioned based on a first synapse address among synapse addresses, and each of the partitions is indexed based on a second synapse address among the synapse addresses.