The present invention relates to an optical signal processing device that can be applied to optical reservoir computing.
In recent years, an environment has been constructed to acquire a large amount of data from various sensors via the Internet, and research and business for analyzing the large amount of acquired data and performing highly accurate knowledge processing and future prediction have been actively carried out. In general, because analysis of a large amount of data requires time and incurs costs such as power consumption, computing devices having high speed and high efficiency are required. As a computing scheme for such information processing, an optical computing technique called reservoir computing (RC), which imitates signal processing of the cerebellum, has been proposed. Optical computing devices using a dynamical system are attracting attention because such devices are likely to have both high speed and high efficiency.
In examples of applications of optical RC in the related art, examples of solving a one-dimensional input and output problem such as a chaos approximation problem and NARMA 10 have mainly been reported (for example, see Non Patent literature 1). Further, in order to meet recent demands for data analysis, it will be necessary to extend the application range of optical RC, and there is a demand for the extension of the input and output problem to multiple dimensions. However, when a method taken in a one-dimensional input problem is simply developed, the number of modulators and demodulators that modulate and demodulate input and output data monotonically increases with an increase in the number of dimensions. Thus, with an increase in the number of input and output dimensions, a complicated and large-scale computation device is required.
In optical RC of the related art, because the number of modulators and demodulators increases with an increase in the number of dimensions of input and output, a computation device becomes complicated and large-scaled, and thus, there is a problem that the manufacturing cost of the device increases.
An object of the present invention is to provide an optical signal processing device capable of performing computation without changing a device configuration even when the number of input and output dimensions changes.
In order to achieve such an object, an aspect of the present invention is an optical signal processing device for converting an input M (M is an integer equal to or greater than 2)-dimensional input signal to an optical signal to perform signal processing, the optical signal processing device including: an input unit configured to convert the input M-dimensional input signal to a one-dimensional input signal, and perform linear processing on the one-dimensional input signal to convert the one-dimensional input signal to an optical signal; a reservoir unit connected to an output of the input unit and configured to perform linear processing and nonlinear processing on the optical signal; and an output unit connected to an output of the reservoir unit and configured to convert the optical signal to an electrical signal to perform linear processing to output an N-dimensional output.
As described above, according to the present invention, by compressing an M-dimensional input signal into a one-dimensional input signal in the input unit, it is possible to perform computation without changing a device configuration even when the number of input and output dimensions changes.
Hereinafter, an embodiment of the present invention will be described in detail with reference to the drawings.
The optical signal processing device of the embodiment compresses M-dimensional data into one-dimensional data in the input unit, and thus, it is possible to perform computation using the same device configuration even when input and output of different numbers of dimensions are required.
Input Unit
A conversion operation in the input unit will be described with reference to
Examples of weighting at the time of compression of data include a method of multiplying randomly determined weights, an averaging method, a method of extracting a maximum value, and a method of extracting a minimum value.
A case in which the input signal is distributed from 1 input channel to m nodes of the reservoir layer in normal RC is considered. Here, the input channel refers to the number of elements included in the one-dimensional input signal, and is a number corresponding to the number of pixels included in the one-dimensional input signal when an input signal is pixel data. The optical modulation unit 112 generates a time-series signal obtained by extending the one-dimensional input signal K-fold in the time axis direction for each input channel. For example, when one input channel is one second, a pulse having a pulse width of K seconds is generated. A subscript K is 1<K<=m, and is intended to cause K nodes to be selected from among m nodes of normal RC and cause a one-dimensional input signal to be input.
A randomly determined weight winlm of the input unit is then multiplied with the extended time-series signal. A subscript 1 is a type of the one-dimensional signal in the first layer corresponding to pixel data for one row of the two-dimensional image signal, and is N in the second and subsequent layers. This allows a pulse extended to K seconds to be a modulation signal having a different intensity for one second. The optical modulation unit 112 modulates the optical signal from the light source 113 with a modulation signal having information of winlm·um.
Thus, the input unit 11 outputs, to the reservoir unit 12, a pulse train in which K pulses having a light intensity corresponding to a magnitude (intensity) of the input signal are connected by the number of rows or columns of the two-dimensional image signal for each input channel.
For the light source 113, an incoherent light source or a coherent light source can be used. When the former is used, the light source can be operated relatively stably because only intensity information is used. When the latter is used, an amount of information can be doubled because both intensity information and phase information are used.
For the optical modulation unit 112, an optical attenuator such as an LN modulator or an optical amplifier such as a semiconductor optical amplifier can be used. When the former is used, it is possible to shorten a computing time because modulation can be performed at a high speed. When the latter is used, it is possible to curb deterioration of computing capability due to a loss because a signal can be amplified.
A weight win of the input unit is given before training of optical RC starts, and a value of the weight is not updated through training or a determination. All values of respective elements of the weight win (weight win=>0) may be different values, or may be the same value for the same m. Although the number of weights differs between the first layer and the second layer, the weights may be different or some of the weights may be the same.
Reservoir Unit
A dynamic system in the reservoir unit 12 is shown in Equation (1).
Math. 1
x
l(t)=cos2(ΣmKwlmin·um(t)+ΣkKwlkr·xk(t−1)) (1)
Here, um is an input channel of the input unit 11 and corresponds to a node of the input layer, winlm corresponds to a weight of the input unit, xk(t−1) corresponds to a node of the reservoir layer when a pulse circulates around the waveguide 124 t−1 times, wrlk represents a weight of the reservoir unit, and xi corresponds to m nodes of the reservoir layer. Among components input to a cos square function of Equation (1), a first term indicates a signal coupled from the input unit 11, and a second term indicates a signal coupled from the reservoir unit 12. A weight wr of the reservoir unit is a fixed value that is randomly determined, like the weight of the input unit. The optical computation processing unit 122 performs linear processing for multiplying the weight wr of the reservoir unit and nonlinear processing for performing computation of a nonlinear function (a cos square function). The weight wr of the reservoir unit is a fixed value that is randomly determined, as in the input unit.
For the optical computation processing unit 122, methods of performing linear processing include a method of using an LN modulator and a delay circuit, and a method of demodulating a signal with an electrical signal temporarily, performing electrical computation processing using a PC, an FPGA, or the like, and then performing restoration of an optical signal. When the former is used, a processing speed becomes higher because the computation is performed at the speed of light. When the latter is used, it is possible to ensure computing accuracy because signal compensation can be performed when conversion to electricity is performed.
The optical computation processing unit 122 can use, for example, a Mach-Zehnder interferometer or a semiconductor optical amplifier in order to perform the nonlinear processing. When the former is used, power consumption is reduced because nonlinear processing is performed with only passage through the Mach-Zehnder interferometer without using a control signal. When the latter is used, it is possible to change a form of the nonlinear function from the cos square function by changing a current value input to the semiconductor optical amplifier, and to perform adjustment to an appropriate nonlinear function for a problem to be solved.
For the merging unit 123, for example, a planar optical waveguide (PLC) or a fusion-extended fiber coupler can be used. When the former is used, it is possible to reduce a connection loss and construct a device with a low loss. When the latter is used, it is possible to easily construct the device by combining commercially available products.
Specific Operation Example
A specific operation example of the reservoir unit when K=m will be described with reference to
The ring waveguide 124 may be extended in length so that K (=m) or more pulses can circulate at the same time in consideration of extensibility. In this case, in the pulse train output from the input unit 11, an idle period of time corresponding to the extended length is inserted into every K pulses. Thus, the two-dimensional image signal divided in nine is processed by the m nodes of the reservoir layer.
Connection Form of Input Unit and Reservoir Unit
The merging units 121a to 121c of the reservoir unit 12 adjust a timing so that the one-dimensional input signals propagating from the input unit 11 overlap on the ring waveguide 124, and output the one-dimensional input signals. Although the device configuration is complicated, the number of circulations is decreased due to the optical pulse trains being caused to overlap in the reservoir unit 12, and thus, it is possible to perform computation at a higher speed.
It is not necessary for the configuration of the reservoir unit 12 to be changed, and it is possible to simplify a device configuration, unlike the first example.
Output Unit
A dynamic system in the output unit 13 is shown in Equation (2).
Math. 2
y
j(t)=Σkmwjk0·xk(t) (2)
Here, yj corresponds to a node of the output layer, and w0jk is a weight of the output unit. The electrical computation processing unit 132 extracts m of the one-dimensional signals xk(t) output from the demodulation unit at a time to compute linear combination shown in Equation (2). The computation is repeated a number of times corresponding to the number N of categories to be classified, and an N-dimensional output is generated from the m signals. A weight w0 of the output unit is a value that is calculated by a pseudo-inverse matrix method using a node xk(t) of the reservoir unit 12 and a desired result of the problem to be solved. This value is different for each layer.
For the demodulation unit 131, a light receiver is used. For the electrical computation processing unit 132, a PC and an FPGA, for example, can be used. When the former is used, it is possible to implement a dynamical system relatively easily. When the latter is used, it is possible to increase a computing speed because a dedicated machine can be manufactured.
Modification Example of Output Unit
The electrical computation processing unit 132 extracts m B one-dimensional signals xk(t) output from the demodulation unit at a time to compute the linear combination shown in Equation (2). The computation is repeated a number of times corresponding to the number N of categories to be classified, and an N-dimensional output is generated from the m B signals. This B is the number of circulations of the reservoir unit 12, and m B signals emitted while the pulse train circulates around the reservoir unit 12 B times are input to the electrical computation processing unit 132. A weight w° of the output unit is a value that is calculated by a pseudo-inverse matrix method using the m×B signals obtained from the reservoir unit 12 and a desired classification result of input data.
For example, considering a case in which data of 3×3 pixels is input row by row as illustrated in
With the optical signal processing device of the embodiment, it is possible to perform computation using the same device configuration even when input and output of different numbers of dimensions are required. Because it is not necessary for the number of modulators and demodulators to be increased even when the number of input and output dimensions increases, it is possible to curb an increase in manufacturing cost of the device, unlike optical RC of the related art.
Number | Date | Country | Kind |
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2018-155747 | Aug 2018 | JP | national |
Filing Document | Filing Date | Country | Kind |
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PCT/JP2019/031972 | 8/14/2019 | WO | 00 |