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Computer system and computation method using recurrent neural network

A recurrent neural network, computer system technology, applied in neural learning methods, biological neural network models, neural architectures, etc., can solve the problems of complex recurrent neural network learning process and high computational cost

Active Publication Date: 2018-10-23
HITACHI LTD
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0008] The recurrent neural network has the problem that the learning process is more complicated than the feedforward network
In addition, there is the problem of high computational cost of the learning process

Method used

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  • Computer system and computation method using recurrent neural network
  • Computer system and computation method using recurrent neural network
  • Computer system and computation method using recurrent neural network

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0090] figure 1 It is a diagram showing a configuration example of the computer 100 for realizing the reserve pool calculation of the first embodiment.

[0091] The computer 100 has a computing device 101 , a memory 102 and a network interface 103 .

[0092] The computing device 101 executes processing according to a program. A processor, an FPGA (Field Programmable Gate Array, Field Programmable Gate Array) and the like can be considered as the computing device 101 . Predetermined functional units are realized by the arithmetic unit 101 executing processing according to the program. In the following description, when the processing is described using a functional unit as a subject, it means that the computing device 101 executes a program that realizes the functional unit.

[0093] The memory 102 stores programs executed by the computing device 101 and information used by the programs. Furthermore, the memory 102 includes a work area temporarily used by programs.

[009...

Embodiment 2

[0166]In Embodiment 1, the input unit 111 , the reservoir unit 112 , and the output unit 113 are realized as software, whereas in Embodiment 2, they are realized using hardware. Hereinafter, details of Embodiment 2 will be described.

[0167] The nonlinear node 200 of the reservoir unit 112 can be realized using hardware such as electronic circuits and optical elements. As an electronic circuit, a Macky-Glass circuit and a source-drain current of a MOSFET can be used. As the optical element, an MZ interferometer and an optical waveguide showing nonlinear characteristics such as saturated absorption can be used.

[0168] In Embodiment 2, a computer in which the reserve tank unit 112 is realized using an optical waveguide will be described.

[0169] Optical devices have high-speed communication and network characteristics with low propagation loss in optical waveguides, so they can be flexibly applied to high-speed processing with reduced power consumption.

[0170] In the ca...

Embodiment 3

[0196] In Embodiment 3, a computer 100 that realizes the reserve pool calculation of Embodiment 1 using an optical circuit chip will be described.

[0197] Figure 8 It is a figure showing an example of the structure of the optical circuit chip of Example 3. in addition, Figure 8 Corresponding to the top view of the optical circuit chip.

[0198] The optical circuit chip 800 mounts a plurality of functional chips on a substrate 801 . In addition, since the optical circuit is mounted in the stacking direction relative to the electronic circuit, optical elements such as the MZ modulator and photodiode do not appear in the drawings.

[0199] The optical circuit chip 800 includes a substrate 801, a silicon nitride optical circuit 802, a silicon optical circuit 803, a substrate 804, a sampling circuit 805, a masking circuit 806, a delay circuit 807, a modulator driving circuit 808, a recursive signal amplifier 809, and a transimpedance amplifier 810 , a readout circuit 811 and...

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PUM

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Abstract

The invention discloses a computer system and a computation method using a recurrent neural network. A computer system that executes computation processing using a recurrent neural network constitutedwith an input unit, a reservoir unit, and an output unit. The input unit includes an input node that receives a plurality of time-series data, the reservoir unit includes a nonlinear node accompanying time delay, the output unit includes an output node calculating an output value. The input unit calculates a plurality of input streams by executing sample and hold processing and mask processing ona plurality of received time-series data, executes time shift processing that gives deviation in time to each of the plurality of input streams and superimposes the plurality of input streams subjected to the time shift processing, thereby calculating input data. High precision and high speed processing can be realized, in reservoir calculating that takes a plurality of time-series data as inputto carry out processing.

Description

technical field [0001] The present invention relates to reservoir computing. Background technique [0002] In recent years, neural networks imitating brain neural networks have been used in machine learning. A neural network consists of an input layer, an output layer, and a hidden layer. In the hidden layer, by repeating simple transformations, input data is transformed into high-dimensional data, and desired outputs such as identification and prediction of information can be obtained. [0003] As an example of the transformation of the hidden layer, there is a nonlinear transformation that imitates the firing phenomenon of neurons. The discharge phenomenon of neurons is known to be a nonlinear phenomenon in which when a potential exceeding a threshold value is input to a neuron, the membrane potential rises sharply and the output changes. In order to reproduce the aforementioned phenomenon, for example, a sigmoid function shown in the formula (1) is used. [0004] 【For...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06N3/063G06N3/04
CPCG06N3/049G06N3/063G06N3/0675G06N3/08G06F16/2477G06N3/044G06F17/18G06N3/04
Inventor 奥村忠嗣田井光春高桥宏昌安藤正彦龟代典史永田真斗
Owner HITACHI LTD