TECHNICAL FIELD

The present invention relates to a product-sum operation device, a neuromorphic device, and a method for using the product-sum operation device. Priority is claimed on Japanese Patent Application No. 2017-254662, filed Dec. 28, 2017, the content of which is incorporated herein by reference.

BACKGROUND ART

In the related art, a learning scheme optimized for grayscale image recognition in an RRAM (registered trademark)-based analog neuromorphic system is known (see, for example, Non-Patent Document 1). This literature describes development of an analog neuromorphic system on the basis of an assembled resistance switching memory array. This literature proposes a new training scheme to optimize the performance of analog systems by utilizing segmented synaptic behaviors. This literature also applies the scheme to grayscale image recognition.

Research has also been conducted to realize a neural network imitating a nervous system by using an array of resistance change elements. A neuromorphic device (NMD) performs a product-sum operation to product weight and sum values from the previous stage to the next stage. Thus, development of various types of product-sum operation devices, each of which combines a plurality of resistance change elements whose resistances change continuously, performs product operations on input signals using their resistance values as weights, and performs sum operation to obtain the sum of currents output from the resistance change elements, and NMDs using the product-sum operation devices has been promoted.

A multilayer perceptron widely used in neural networks includes an input layer, one or more hidden layers, and an output layer, each of which is associated with an appropriate weight (coupling strength) and bias term (see, for example, Non-Patent Document 2).

CITATION LIST

Patent Literature

[Patent Document 1]

PCT International Publication No. WO2017/183573

[Non-Patent Document]

[Non-Patent Document 1]

Zhe Chen et al., “Optimized Learning Scheme for Grayscale Image Recognition in a RRAM Based Analog Neuromorphic System,” 2015, IEEE, p. 17.7.1-p. 17.7.4

[Non-Patent Document 2]

Sebastian Raschka, “Python Machine Learning Programming,” in press, p. 328-330

SUMMARY OF INVENTION

Technical Problem

However, Non-Patent Document 1 does not discuss how to configure bias term elements. When a bias term element has malfunctioned, the performance of the neural network may be greatly degraded depending on the configuration of the bias term element. This is because the value of “weight” is generally set for each specific combination, whereas the bias term is used to bias all values of the layer.

In view of the above problems, it is an object of the present invention to provide a product-sum operation device, a neuromorphic device, and a method for using the product-sum operation device which can, when applied to a neural network, curb a reduction in the performance of the neural network when a bias term element has malfunctioned.

Solution to Problem

A product-sum operation device of an aspect of the present invention includes a product operator and a sum operator, wherein the product operator includes a plurality of variable-input product operation elements and a plurality of fixed-input product operation elements, each of the plurality of variable-input product operation elements and the plurality of fixed-input product operation elements is a resistance change element, the product-sum operation device further includes a variable input unit configured to input a variable signal to the plurality of variable-input product operation elements, and a fixed input unit configured to input a determined signal to the plurality of fixed-input product operation elements in synchronization with the variable signal, and the sum operator includes an output detector configured to detect a sum of outputs from the plurality of variable-input product operation elements and outputs from the plurality of fixed-input product operation elements.

In the product-sum operation device of an aspect of the present invention, the plurality of fixed-input product operation elements may have a function of being disconnected when an output current increase malfunction has occurred.

In the product-sum operation device of an aspect of the present invention, the resistance change element may have a write terminal, a common terminal, and a read terminal.

In the product-sum operation device of an aspect of the present invention, the resistance change element may be a magnetoresistance effect element exhibiting a magnetoresistance effect, and the magnetoresistance effect element may include a magnetization free layer having a domain wall, a magnetization fixed layer whose magnetization direction is fixed, and a nonmagnetic layer sandwiched between the magnetization free layer and the magnetization fixed layer.

In the product-sum operation device of an aspect of the present invention, the product operator may further include another variable-input product operation element and another fixed-input product operation element, each of the other variable-input product operation elements and the other fixed-input product operation elements may be the resistance change element, the variable input unit may be configured to input the variable signal to the other variable-input product operation element, the fixed input unit may be configured to input the determined signal to the other fixed-input product operation element in synchronization with the variable signal, and the sum operator may further include another output detector configured to detect a sum of an output from the other variable-input product operation element and an output from the other fixed-input product operation element.

An aspect of the present invention is a neuromorphic device including the product-sum operation device.

An aspect of the present invention is a method for using the product-sum operation device, wherein the product-sum operation device further includes a malfunction diagnosis unit, and the method includes an output detection process in which the output detector detects outputs from the plurality of fixed-input product operation elements in a state in which the variable input unit does not input the variable signal to the plurality of variable-input product operation elements and the fixed input unit inputs the determined signal to the plurality of fixed-input product operation elements, and a diagnosis process in which the malfunction diagnosis unit determines whether or not a malfunctioning product operation element is included in the plurality of fixed-input product operation elements on the basis of the outputs from the plurality of fixed-input product operation elements detected by the malfunction diagnosis unit in the output detection process.

In the method for using the product-sum operation device of an aspect of the present invention, the product-sum operation device may further include a relearning unit, and the method may further include a relearning process in which when the malfunction diagnosis unit has determined that a malfunctioning product operation element is included in the plurality of fixed-input product operation elements, the relearning unit resets a resistance value of a product operation element other than the malfunctioning product operation element among the plurality of fixed-input product operation elements.

Advantageous Effects of Invention

According to the present invention, it is possible to provide a product-sum operation device, a neuromorphic device, and a method for using the product-sum operation device which can, when applied to a neural network, curb a reduction in the performance of the neural network when a bias term element has malfunctioned.

