Abstract: Disclosed is an interface system including a first neuron cluster that outputs a first neuron signal including a first neuron request and first neuron data by performing a first arithmetic operation, and a first interface circuit that stores the first neuron data and outputs a first response, in response to the first neuron request. The first neuron cluster outputs a second neuron signal including a second neuron request and second neuron data by performing a second arithmetic operation, in response to the first response. Before the first data is transmitted to a second neuron cluster different from the first neuron cluster, the first interface circuit outputs the first response in response to a fact that the first neuron data is stored.

Abstract: Disclosed are a fingerprint forgery detection device and a method of operating the same. The fingerprint forgery detection device includes a memory that stores a first feature signal including biological channel feature information of a user, a transmitter including at least one transmission electrode for transmitting a pulse signal to the user, a receiver including at least one reception electrode for receiving a biological channel response signal in response to the transmitted pulse signal, and a signal processor for processing the biological channel response signal to detect whether a fingerprint is forged, and at least one processor that controls the memory, the transmitter, and the receiver.

Abstract: Disclosed is an amplification circuit, which includes a first amplifier that receives an external signal and performs first band pass filtering on the external signal to output a first filter signal, and a second amplifier that receives the first filter signal and performs second band pass filtering on the first filter signal to output a second filter signal, and a frequency pass bandwidth of the second band pass filtering is narrower than a frequency pass bandwidth of the first band pass filtering.

Abstract: A hybrid communication device, an operation method thereof, and a communication system including the same are provided. The hybrid communication device includes a contact unit that includes an antenna for receiving a first communication signal and an electrode for receiving a second signal, a switch controller that includes a first switch and a second switch and controls the first switch and the second switch based on a change in capacitance of the electrode, and a signal processing unit that receives at least one of the first communication signal and the second communication signal from the contact unit via the first switch and processes the received signal. The first switch is connected to the contact unit, and the signal processing unit is connected to the first switch.

Abstract: Provided is a spike neural network circuit including a synapse configured to generate an operation signal based on an input spike signal and a weight, and a neuron configured to generate an output spike signal using a comparator configured to compare a voltage of a membrane signal generated based on the operation signal with a voltage of a threshold signal, wherein the comparator includes a bias circuit configured to conditionally supply a bias current of the comparator depending on the membrane signal.

Abstract: Disclosed is a spike neural network circuit which includes a pulse generator that receives an input spike signal and generates a first modulation pulse and a second modulation pulse based on the input spike signal, first and second current source arrays controlled based on a weight memory, a membrane capacitor, a first switch that delivers a first calculation signal generated from the first current source array to the membrane capacitor, in response to the first modulation pulse, and a second switch that delivers a second calculation signal generated from the second current source array to the membrane capacitor, in response to the second modulation pulse.

Abstract: Disclosed is a spike neural network circuit including a synaptic circuit including synapses arranged in rows and columns, an axon circuit that generates a first input spike signal to be provided to a first row among the rows, and a second input spike signal to be provided to a second row among the rows, an input spike detecting circuit that generates an enable signal when detecting a pulse from at least one of the first input spike signal and the second input spike signal, and a first neuron circuit that compares a voltage level of a first accumulated signal, which is output from a first column among the columns, with a threshold voltage level in response to the enable signal, and outputs a first output spike signal when the voltage level of the first accumulated signal exceeds the threshold voltage level.

Abstract: Disclosed is a spike neural network circuit including a weight storage that receives an input spike signal and outputs data based on a weight, a charge sharing synaptic circuit that generates a synaptic voltage based on the output data, a switched capacitor circuit that naturally discharges the generated synaptic voltage, a voltage-to-current conversion circuit that receives the synaptic voltage and generates a membrane voltage, and a neuron circuit that receives the membrane voltage and a threshold voltage and generates an output spike signal based on the received membrane voltage and the received threshold voltage.

Abstract: Disclosed is a neuron circuit, which includes a first bias circuit that adds a bias current to an input current to generate a biased input current, a logarithm-based neuron calculation circuit that performs a logarithm calculation on an amount of current of the biased input current to generate an input logarithm value and generates a biased output voltage by performing a logarithm-based Hodgkin-Huxley model calculation based on the input logarithm value, and a second bias circuit that adds a bias voltage to the biased output voltage to generate an output voltage.

Abstract: A neuron circuit including a first internal circuit that receives a plurality of spike input signals, generates a first sum value by summing a plurality of synaptic weights corresponding to the plurality of spike input signals, and outputs a second sum value by adding a membrane potential value to the first sum value, a spike generating circuit that generates a spike output signal, a membrane potential generating circuit that generates the membrane potential value, a second internal circuit that counts a last spike time based on the spike output signal, and an online learning circuit that receives a last input time from the first internal circuit and performs LTP learning based on the last input time or receives the last spike time from the second internal circuit and performs LTD learning based on the last spike time.

Abstract: Disclosed is a spike neural network circuit including an axon circuit that generates a first input spike signal, a conversion table that converts a first fire probability of a first neuron corresponding to the first input spike signal into a first threshold value, and a probabilistic operator. The probabilistic operator includes a random number generator that generates a random number value based on an event that the first input spike signal is at a first logic level, a random number comparator that generates a first comparison signal by comparing the first threshold value with the random number value, and a spike generator that generates an output spike signal corresponding to the first neuron based on an event that the first comparison signal is at the first logic level.

Abstract: Provided is a capsule endoscope. The capsule endoscope includes: an imaging device configured to perform imaging on a digestive tract in vivo to generate an image; an artificial neural network configured to determine whether there is a lesion area in the image; and a transmitter configured to transmit the image based on a determination result of the artificial neural network.

Abstract: Disclosed are a biometric authentication device and a fingerprint authentication device including the same. The biometric authentication device includes a clock generator that generates a clock signal, an authentication signal generator that generates an authentication signal based on the clock signal, a transmission electrode that transmits the authentication signal to a contact body, a reception electrode that receives a channel pass signal in which the authentication signal passes through a channel of the contact body, and a biometric authenticator that restores the channel pass signal and compares the restored channel pass signal with the authentication signal to generate a biometric authentication signal indicating whether the contact body is a biological tissue. According to the present disclosure, it is possible to improve the security of a fingerprint authentication device.

Abstract: The human body sensing device includes a contact sensing unit that includes a sensing electrode and a signal electrode, an activation module that senses a contact with a body through the sensing electrode when the sensing electrode and the signal electrode contact the body and outputs a wake-up signal in response to the sensing of the contact, and a human body communication unit that provides a ground voltage to the signal electrode and outputs a data signal to the signal electrode when the wake-up signal from the activation module is received.

Abstract: Disclosed is an electronic device that supports a neural network including a neuron array including neurons, a row address encoder that receives spike signals from neurons and outputs request signals in response to the received spike signals, and a row arbiter tree that receives request signals from the row address encoder and outputs response signals in response to the received request signals. The row arbiter tree includes a first arbiter that arbitrates first and second request signals among request signals, a first latch circuit that stores a state of the first arbiter, a second arbiter that arbitrates third and fourth request signals among request signals, a second latch circuit that stores a state of the second arbiter, and a third arbiter that delivers a response signal to the first and second arbiters based on information stored in the first and second latch circuits.

Abstract: Disclosed is operation method of an encoder that receives a continuous time-series signal and respectively transmits first to N-th input signals to first to N-th input neuron circuits of spike neural network circuit. The method of operating the encoder includes receiving the continuous time-series signal, generating a plurality of discrete quantum signals by sampling and quantizing the continuous time-series signal, selecting first to N-th discrete quantum signals among the plurality of discrete quantum signals, matching the selected first to N-th discrete quantum signals with the first to N-th input neuron circuits, respectively, identifying discrete quantum signals, each of which has a quantum level different from a quantum level of a previous discrete quantum signal, from among the second to N-th discrete quantum signals, and activating the input signals to be transmitted to the input neuron circuits corresponding to the identified discrete quantum signals and the first discrete quantum signal.

Abstract: Provided are a capsule endoscopic receiving device, a capsule endoscope system including the same, and an operating method of the capsule endoscopic receiving device, the capsule endoscopic receiving device including an analog front end configured to receive a preamble from one receiving electrode pair from among a plurality of receiving electrodes, a valid signal detection circuit configured to compare a reference voltage with input data generated on a basis of a voltage level of the preamble, and a preamble processor configured to select a final electrode pair configured to receive the image data on a basis of a correlation value of the preamble and a comparison result of the input data and the reference voltage. According to the inventive concept, stability of receiving image data may be secured by selecting an optimal receiving electrode pair.

Abstract: Disclosed are a biosignal receiving device and a method thereof. The biosignal receiving device used in a communication system using a human body as a medium includes a receiving electrode unit including a plurality of receiving electrodes, an input selection unit including two MUXs for selecting one of the plurality of receiving electrodes, and that output biosignals received by the selected electrodes, a filter that removes noise included in the biosignals and output filtered signals from which the noise is removed, a differential amplifier that amplifies a difference between the filtered signals and output amplified signals, a CDR circuit that generates a data signal and a clock signal from the amplified signals and outputs the data signal and the clock signal, and a controller that output selection signals for selecting one of the plurality of receiving electrodes, based on the data signal and the clock signal.

Abstract: Disclosed is an encoder including event layer outputs first and second event signals, weight layer applies first and second weights to the first and second event signals respectively, and provides the first event signal in which the first weight is applied and the second event signal in which the second weight is applied to first node, and first spike generation circuit generates first input spike signal of which firing period is changed based on voltage level of the first node. The voltage level of the first node is reduced continuously, increases for first voltage corresponding to the first weight in response to the first event signal activated, and increases for second voltage corresponding to the second weight in response to the second event signal activated.

Abstract: Disclosed are a spike neural network apparatus based on a multi-encoding and an operating method thereof. The method of operating a spike neural network (SNN) apparatus that performs a multi-encoding, includes receiving an input signal by an encoding module, performing a rate coding and a temporal coding on the received input signal by the encoding module, generating an SNN input signal based on the performance result of the rate coding and the temporal coding, and transmitting the generated SNN input signal to a neuromorphic chip that performs a spike neural network (SNN) operation.