Abstract: In a gate electrode of a nonvolatile memory device of an embodiment, a tunnel insulating film covers a channel region. A first current collector file is disposed on the side opposite to the channel region with respect to the tunnel insulating film. An ion conductor film is disposed between. the tunnel insulating film and the first current collector film. A first electrode film is disposed between the tunnel insulating film and the ion conductor film. The first electrode film is in contact with the ion conductor film. A second electrode film. is disposed between the ion conductor film and the first current collector film. The second electrode film is in contact with the ion conductor film. A second current collector film is disposed between the tunnel insulating. film and the second electrode film.

Abstract: A neural network device according to an embodiment includes an arithmetic circuit, a learning control circuit, and a bias reset circuit. The arithmetic circuit executes arithmetic processing according to a neural network using a plurality of weights each represented by a value of a first resolution and a plurality of biases each represented by a value in ternary. At the time of learning of the neural network, the learning control circuit repeats a learning process of updating each of the plurality of weights and each of the plurality of biases a plurality of times based on a result of the arithmetic processing according to the neural network performed by the arithmetic circuit. In each learning process, the bias reset circuit resets a bias randomly selected with a preset first probability among the plurality of biases to a median in the ternary.

Abstract: According to one embodiment, there is provided a neural network device including a neuron, a conversion part, a transmission part, a control part and a holding part. The conversion part converts a spike signal to a synapse current according to weight. The transmission part transmits the converted synapse current to the neuron. The control part determines transition of a state of the weight. The holding part holds the weight as a discrete state according to the determined transition of the state. The holding part includes an action part that stochastically operates based on a signal input from the control part to cause transition of the state of the weight. A cumulative probability of actions of the action part changes in a sigmoidal shape with respect to number of signal input times.

Abstract: A storage device according to an embodiment is for storing weights being continuous values. The storage device includes: a shift register, an initialization circuit, an update control circuit, and a readout control circuit. The shift resistor includes a plurality of cells, each being arranged in series and storing information. A position of each of the plurality of cells corresponds to the weight. The initialization circuit writes the information to a cell in the shift register. The update control circuit shifts a position of the cell storing the information in a direction corresponding to a sign of an update amount by a number of cells corresponding to an absolute value of the update amount. The readout control circuit reads out the information and outputs an output value according to the weight corresponding to the position of the cell storing the information.

Abstract: An apparatus of analog-neuron includes a synapse circuit for performing arithmetic processing for multiplying an input signal that arrives at an input terminal by a weight value, a synapse output holding means for holding an output signal of the synapse circuit, and a power control unit for controlling whether to supply power at least to the synapse circuit or to stop supplying power in response to whether the input signal has arrived at the input terminal or has been lost.

Abstract: An arithmetic device includes N product-sum-operation circuits, a control circuit, and an output circuit. Each product-sum-operation circuit outputs intermediate signals obtained by binarizing a product-sum-operation value obtained by product-sum-operation of M input values of M input signals and M weight values. The control circuit inverts positive/negative of each M weight value at determining-timing when a given time elapses from input timing. Based on a delay time from the determination-timing to logic finalization of the intermediate signal for each N product-sum-operation circuit, the output circuit outputs an output signal representing a winner-product-sum-operation circuit for which the product-sum-operation value having a sign and the largest absolute value is calculated.

Abstract: A storage device according to an embodiment is for storing weights being continuous values. The storage device includes: a shift register, an initialization circuit, an update control circuit, and a readout control circuit. The shift resistor includes a plurality of cells, each being arranged in series and storing information. A position of each of the plurality of cells corresponds to the weight. The initialization circuit writes the information to a cell in the shift register. The update control circuit shifts a position of the cell storing the information in a direction corresponding to a sign of an update amount by a number of cells corresponding to an absolute value of the update amount. The readout control circuit reads out the information and outputs an output value according to the weight corresponding to the position of the cell storing the information.

Abstract: An arithmetic apparatus according to an embodiment outputs a multiplicative value obtained by multiplying a weight value and an input value. The arithmetic apparatus includes a memristor, a logarithmic transform circuit, and a current-voltage converter circuit. The memristor is a device capable of changing voltage-current characteristic, and the memristor is preset to voltage-current characteristic according to the weight value. The logarithmic transform circuit applies an intermediate voltage, to the memristor, that is obtained by logarithmically transforming an input voltage according to the input value in accordance with a logarithmic transform function obtained by multiplying a natural logarithm function by a preset coefficient. The current-voltage converter circuit outputs an output voltage obtained by performing current-voltage conversion of current flowing through the memristor according to a preset linear function, as a multiplicative value.

Abstract: A synaptic circuit according to an embodiment includes: a weight current circuit that applies a weight current corresponding to a weight value; an input switch that switches whether or not to cause the weight current circuit to apply the weight current; a capacitor that includes a first terminal and a second terminal, the first terminal being given a constant voltage; an output circuit that outputs the output signal corresponding to a capacitor voltage; a charge adjustment circuit that decreases or increases charges accumulated in the capacitor by drawing, from the second terminal, a capacitor current corresponding to a current value of the weight current, or supplying the capacitor current to the second terminal; and a control circuit that switches whether or not to reduce a current having a predetermined current value from the capacitor current in accordance with the weight value.

Abstract: A spiking neural network device according to an embodiment includes a synaptic element, a neuron circuit, a determinator, a synaptic depressor, and a synaptic potentiator. The synaptic element has a variable weight and outputs, in response to input of a first spike signal, a synaptic signal having intensity adjusted in accordance with the weight. The neuron circuit outputs a second spike signal in a case where the synaptic signal is inputted and a predetermined firing condition for the synaptic signal is satisfied. The determinator determines whether or not the weight is to be updated on a basis of an output frequency of the second spike signal by the neuron circuit. The synaptic depressor performs depression operation for depressing the weight in a case where it is determined that the weight is to be updated. The synaptic potentiator performs potentiating operation for potentiating the weight.

Abstract: According to an embodiment, an arithmetic device includes a comparator, M cross switches, and M coefficient circuits. The comparator compares a first voltage generated at a first comparison terminal and a second voltage generated at a second comparison terminal. The M cross switches are provided corresponding to the M input signals. The M coefficient circuits are provided corresponding to the M coefficients, and each includes a first constant current source and a second constant current source. Each of the M cross switches performs switching between a straight state and a reverse state. In each of the M coefficient circuits, the first constant current source is connected between a positive output terminal of the corresponding coefficient circuit and a reference potential, and the second constant current source is connected between a negative output terminal of the corresponding coefficient circuit and the reference potential.

Abstract: According to an embodiment, an inference system includes a recurrent neural network circuit, an inference neural network, and a control circuit. The recurrent neural network circuit receives M input signals and outputs N intermediate signals, where M is an integer of 2 or more and N is an integer of 2 or more. The inference neural network circuit receives the N intermediate signals and outputs L output signals, where L is an integer of 2 or more. The control circuit adjusts a plurality of coefficients that are set to the recurrent neural network circuit and adjusts a plurality of coefficients that are set to the inference neural network circuit. The control circuit adjusts the coefficients set to the recurrent neural network circuit according to a total delay time period from timing for applying the M input signals until timing for firing the L output signals.

Abstract: A spiking neural network device according to an embodiment includes a synaptic element, a neuron circuit, a synaptic potentiator, and a synaptic depressor. The synaptic element has a variable weight. The neuron circuit inputs a spike voltage having a magnitude adjusted in accordance with the weight of the synaptic element via the synaptic element, and fires when a predetermined condition is satisfied. The synaptic potentiator performs a potentiating operation for potentiating the weight of the synaptic element depending on input timing of the spike voltage and firing timing of the neuron circuit. The synaptic depressor performs a depression operation for depressing the weight of the synaptic element in accordance with a schedule independent from the input timing of the spike voltage and the firing timing of the neuron circuit.

Abstract: A calculation device includes: M coefficient storage units provided corresponding to the M coefficients, each of the M coefficient storage units including a positive-side coefficient and a negative-side coefficient representing a coefficient corresponding to a sign of a difference; M multiplication units provided corresponding to the M input values, each of the M multiplication units calculating a positive-side multiply value obtained by multiplying the positive-side coefficient included in the corresponding coefficient storage unit by a sign inverted according to the corresponding input value and a negative-side multiply value obtained by multiplying the negative-side coefficient included in the corresponding coefficient storage unit by a sign inverted according to the corresponding input value; and an output unit outputting an value according to a difference between a positive-side sum value obtained by summing the M positive-side multiplication values and a negative-side sum value obtained by summing the M

Abstract: An arithmetic apparatus according to an embodiment outputs a multiplicative value obtained by multiplying a weight value and an input value. The arithmetic apparatus includes a memristor, a logarithmic transform circuit, and a current-voltage converter circuit. The memristor is a device capable of changing voltage-current characteristic, and the memristor is preset to voltage-current characteristic according to the weight value. The logarithmic transform circuit applies an intermediate voltage, to the memristor, that is obtained by logarithmically transforming an input voltage according to the input value in accordance with a logarithmic transform function obtained by multiplying a natural logarithm function by a preset coefficient. The current-voltage converter circuit outputs an output voltage obtained by performing current-voltage conversion of current flowing through the memristor according to a preset linear function, as a multiplicative value.

Abstract: A neural network not requiring massive changes in configuration when changing the number of stages (number of levels) of the neural network. This neural network is provided with at least one neuron core 10 performing an analog multiply-accumulate operation and a weight-value-supply control unit 30 supplying the weight value to the neuron core 10. This neural network is subjected to control processing by a control-processor unit 40 controlling the supply of the weight value from said weight-value-supply control unit 30 in synchronization with the timing of the analog multiply-accumulate operation of the neuron core 10, and processing the data output from the neuron core 10 at every analog multiply-accumulate operation performed by said neuron core as serial data and/or parallel data.

Abstract: According to an embodiment, a multiplier accumulator includes a controller, a high-order multiplier, a high-order accumulator, a low-order multiplier, and an output unit. The controller is configured to designate each digit within a range of the most significant digit in a coefficient for an input value to a stop digit as a target digit. The high-order multiplier is configured to calculate a high-order multiplication value by multiplying the input value, and a value and a weight of the target digit. The high-order accumulator is configured to calculate a high-order accumulation value by accumulatively adding the high-order multiplication values for input values. The low-order multiplier is configured to calculate a low-order multiplication value by multiplying an input value and a value of a digit smaller than the stop digit. The output unit is configured to output a value determined based on whether the high-order accumulation value exceeds a boundary value.

Abstract: According to an embodiment, a neural network apparatus includes a plurality of neuron circuits, each including an integration circuit, a firing circuit, and a secondary battery. The integration circuit is configured to output an integral signal obtained by integrating input signals. The firing circuit is configured to generate, in accordance with the integral signal, a pulse signal to be transmitted to the neuron circuit provided at a subsequent layer. The secondary battery is configured to supply the firing circuit with drive electric power used for generating the pulse signal.

Abstract: According to an embodiment, a neural network apparatus includes cores, routers, a tree path, and a short-cut path. The cores are provided according to leaves in a tree structure, each core serving as a circuit that performs calculation or processing for part of elements of the neural network. The routers are provided according to nodes other than the leaves in the tree structure. The tree path connects the cores and the routers such that data is transferred along the tree structure. The short-cut path connects part of the routers such that data is transferred on a route differing from the tree path. The routers transmit data output from each core to any of the cores serving as a transmission destination on one of routes in the tree path and the short-cut path such that the calculation or the processing is performed according to a structure of the neural network.

Abstract: According to an embodiment, an operation apparatus includes a first neural network, a second neural network, an evaluation circuit, and a coefficient-updating circuit. The first neural network performs an operation in a first mode. The second neural network performs an operation in a second mode and has a same layer structure as the first neural network. The evaluation circuit evaluates an error of the operation of the first neural network in the first mode and evaluates an error of the operation of the second neural network in the second mode. The coefficient-updating circuit updates, in the first mode, coefficients set for the second neural network based on an evaluating result of the error of the operation of the first neural network, and updates, in the second mode, coefficients set for the first neural network based on an evaluating result of the error of the operation of the second neural network.