CN113361189B - Chip performance degradation trend prediction method based on multi-step robust prediction learning machine - Google Patents

Chip performance degradation trend prediction method based on multi-step robust prediction learning machine Download PDF

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CN113361189B
CN113361189B CN202110514211.7A CN202110514211A CN113361189B CN 113361189 B CN113361189 B CN 113361189B CN 202110514211 A CN202110514211 A CN 202110514211A CN 113361189 B CN113361189 B CN 113361189B
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刘震
梅文娟
刘昊天
龙兵
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University of Electronic Science and Technology of China
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Abstract

The invention discloses a chip performance degradation trend prediction method based on a multi-step robust prediction learning machine, which combines an extreme learning machine and a recurrent neural network, has extremely high information fusion capability and information rapid processing capability, simultaneously constructs similarity based on relevant entropy by establishing an error code book, establishes real-time prediction model updating according to chip degradation diversity and dynamics, and overcomes the influence of interference on a prediction result. Therefore, compared with the existing method, the method has higher online prediction precision, and the multi-step prediction result is more accurate compared with the existing method.

Description

Chip performance degradation trend prediction method based on multi-step robust prediction learning machine
Technical Field
The invention belongs to the technical field of electronic device health management and machine learning, and particularly relates to a chip performance degradation trend prediction method based on a multi-step robust prediction learning machine.
Background
Along with the rapidity of chip technology development and the diversity of chip working environments, the existing chip performance degradation trend prediction method faces the challenges of time complexity and technical flexibility. On one hand, along with the improvement of the chip information interaction speed, the estimation quantity of the health state needs to be quickly given by a prediction algorithm; on the other hand, due to the complex and various chip application environments, the prediction algorithm needs to flexibly evaluate the health state according to the actual operation condition of the chip. However, the existing chip performance degradation trend prediction methods usually employ an offline health state model based on physical mechanism modeling or data driving. The degradation trend prediction method is highly dependent on experimental data and empirical data, and the operation complexity caused by training is high. Therefore, the existing degradation trend prediction method cannot meet new requirements on rapidity and flexibility of prediction.
In order to improve the rapidity and flexibility of the degradation trend prediction method, an online degradation trend prediction method is gradually started. The degradation trend prediction method uses real-time data and historical data in the chip work to evaluate the future chip work condition according to the chip operation state. The online degradation trend prediction method can adjust the model according to the real-time response of the electronic element, so that the method has strong flexibility in practical application, and the prediction precision is improved to a certain extent compared with an offline prediction method. However, the existing online degradation trend prediction method still has the following problems in engineering practice: firstly, the existing online degradation trend prediction method cannot ensure that effective information in historical data is efficiently transmitted in a multi-dimensional prediction process, so that part of the effective information cannot play a role in actual prediction; secondly, although the online pre-degradation trend measurement method can make corresponding adjustment according to real-time data information, the parameter adjustment needs iterative operation or re-checking of part of models, and cannot adapt to the running speed requirement of related elements; thirdly, since most models assume that the degradation trend of elements meets the wiener process, the models are easily interfered by non-gaussian noise and singular points in data, and the prediction result is greatly influenced.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide a chip performance degradation trend prediction method based on a multi-step robust prediction learning machine so as to improve the information fusion capability and the information rapid processing capability and overcome the influence of interference on a prediction result.
In order to achieve the purpose, the chip performance degradation trend prediction method based on the multi-step robust prediction learning machine is characterized by comprising the following steps of:
(1) initializing the prediction model
1.1), setting the maximum prediction step number K, and importing training historical data Xhisto＝{x1,x2,…,xt0In which xtThe chip performance characterization parameter data (such as saturation voltage drop of an insulated gate bipolar transistor) recorded by the sensor at the t-th moment is represented by t, 1,2, …, t0 and t0, which are the number of historical data;
structure of initializing recurrent neural network M ═ M1,M2,M3,…,MK}，Mk＝{Xk,Whk,βk,Ek,σkThe neural network module for the k prediction, where XkFor neural network module MkInput of (1), WhkFor neural network module MkHidden layer weight of (1), betakFor neural network module MkOutput layer weight of EkFor neural network module MkOf the code book, sigmakFor neural network module MkThe estimated variance under the codebook record of (1);
obtaining optimal prediction dimension m and delay coefficient t of model input sample by utilizing optimal entropy rate methodd；
Initializing WhkIs a two-dimensional random matrix of m × N, betakIs an N-dimensional zero vector;
1.2), calculating the k step neural network module MkInput vector X ofk：
Figure RE-GDA0003147949260000021
Figure RE-GDA0003147949260000022
Setting a k step neural network module MkThe true outputs of (c) are:
Figure RE-GDA0003147949260000023
1.3) generating a recurrent neural network under offline training as a prediction model
1.3.1), compute hidden layer input Hk：
Figure RE-GDA0003147949260000024
Wherein the content of the first and second substances,
Figure RE-GDA0003147949260000025
is the k step neural network module MkT 1,2, …, t0-k-mtd：
Figure RE-GDA0003147949260000026
Wherein f (.) is a Sigmoid activation function;
1.3.2) generating weights β for output layersk：
Figure RE-GDA0003147949260000031
1.3.3), calculating prediction output
Figure RE-GDA0003147949260000032
1.4) generating an error code book in an off-line training state
1.4.1), statistical neural network module MkCode book under prediction error of
Figure RE-GDA0003147949260000033
1.4.2), estimating neural network module MkEstimated variance under codebook record of (1)
Figure RE-GDA0003147949260000034
Figure RE-GDA0003147949260000035
Wherein med (.) is the median calculation;
(2) real-time multi-step prediction
2.1) initializing the running time ts of the prediction model to be t0+ 1;
2.2) obtaining chip performance characterization parameter data x recorded by the sensorts
2.3), judging whether the prediction program is manually stopped or whether the running time ts reaches the set upper limit value tmaxIf yes, ending the real-time prediction program; if not, entering the step 2.4);
2.4), updating the prediction model and generating real-time prediction data
2.4.1), initializing the prediction step number k to be 1;
2.4.2), judging whether the predicted step number K is larger than the maximum predicted step number K, if so, entering the step 2.5), and if not, entering the step 2.4.3);
2.4.3), generating neural network module MkInput vector of (2):
Figure RE-GDA0003147949260000036
wherein for data at time t
Figure RE-GDA0003147949260000037
The values are:
Figure RE-GDA0003147949260000038
2.4.4), computing k-step prediction data
Figure RE-GDA0003147949260000039
2.4.5), updating prediction inputs of prediction module
Figure RE-GDA00031479492600000310
True output
Figure RE-GDA00031479492600000311
And desired output
Figure RE-GDA00031479492600000312
2.4.6), updating codebook information
2.4.6.1), statistical neural network module MkCode book under prediction error of
Figure RE-GDA00031479492600000313
2.4.6.2), estimating neural network module MkEstimated variance under codebook record of (1)
Figure RE-GDA00031479492600000314
Figure RE-GDA00031479492600000315
Wherein med (.) is the median calculation;
2.4.7), update prediction module output weights
Figure RE-GDA00031479492600000316
Wherein ΛkFor the correlation entropy diagonal matrix:
Figure RE-GDA0003147949260000041
2.4.8), update k ═ k +1, return to step 2.4.2);
2.5) obtaining K-step prediction data of operation time ts
Figure RE-GDA0003147949260000042
And (5) updating ts to be ts +1, and returning to the step 2.2), and predicting the K-step prediction data of the next operation time in real time.
The invention aims to realize the following steps:
the chip performance degradation trend prediction method based on the multi-step robust prediction learning machine combines the extreme learning machine and the recurrent neural network, has extremely high information fusion capability and information rapid processing capability, simultaneously constructs similarity based on relevant entropy by establishing an error code book, establishes real-time prediction model updating according to chip degradation diversity and dynamics, and overcomes the influence of interference on prediction results. Therefore, compared with the existing method, the method has higher online prediction precision, and the multi-step prediction result is more accurate compared with the existing method.
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FIG. 1 is a flowchart of an embodiment of a method for predicting degradation trend of chip performance based on a multi-step robust prediction learning machine according to the present invention;
FIG. 2 is a flowchart of an embodiment of the steps shown in FIG. 1 for updating a predictive model and generating real-time predictive data.
Detailed Description
The following description of the embodiments of the present invention is provided in order to better understand the present invention for those skilled in the art with reference to the accompanying drawings. It is to be expressly noted that in the following description, a detailed description of known functions and designs will be omitted when it may obscure the subject matter of the present invention.
FIG. 1 is a flowchart of an embodiment of a method for predicting a chip performance degradation trend based on a multi-step robust prediction learning machine according to the present invention.
In this embodiment, as shown in fig. 1, the method for predicting the chip performance degradation trend based on the multi-step robust prediction learning machine of the present invention includes the following steps:
step S1: initializing a predictive model
Step S1.1: initializing model parameters
Setting the maximum predicted step number K, and importing training historical data Xhisto＝{x1,x2,…,xt0In which xtSetting maximum prediction step number K for chip performance characterization parameter data (such as saturation voltage drop of insulated gate bipolar transistor) recorded by the sensor at the t-th moment, and importing training historical data Xhisto＝{x1,x2,…,xt0In which xtFor the chip performance characterization parameter data (such as saturation voltage drop of the insulated gate bipolar transistor) recorded by the sensor at the t-th moment, t is 1,2, …, t0, and t0 is the number of historical data.
Structure of initializing recurrent neural network M ═ M1,M2,M3,…,MK}，Mk＝{Xk,Whk,βk,Ek,σkThe neural network module for the k prediction, where XkFor neural network module MkInput of (1), WhkFor neural network module MkHidden layer weight of (1), betakFor neural network module MkOutput layer weight of EkFor neural network module MkOf the code book, sigmakFor neural network module MkThe estimated variance under the codebook record.
Obtaining optimal prediction dimension m and delay coefficient t of model input sample by utilizing optimal entropy rate methodd。
Initializing WhkIs a two-dimensional random matrix of m × N, betakIs an N-dimensional zero vector.
Step S1.2: computing an input vector
Calculating k step neural network module MkInput vector X ofk：
Figure RE-GDA0003147949260000051
Figure RE-GDA0003147949260000052
Setting a k step neural network module MkThe true outputs of (c) are:
Figure RE-GDA0003147949260000053
step S1.3: generating a recurrent neural network under offline training as a prediction model
Step S1.3.1: computing hidden layer input Hk：
Figure RE-GDA0003147949260000054
Wherein the content of the first and second substances,
Figure RE-GDA0003147949260000055
is the k step neural network module MkT-th input of (2) corresponds to a hidden layer input of (1, 2, …, t0-k-mtd：
Figure RE-GDA0003147949260000056
Wherein f (.) is a Sigmoid activation function;
step S1.3.2: generating weights beta of output layersk：
Figure RE-GDA0003147949260000057
Step S1.3.3: computing predicted outputs
Figure RE-GDA0003147949260000058
Step S1.4: generating an error codebook in an offline training state
Step S1.4.1: statistical neural network module MkCode book under prediction error of
Figure RE-GDA0003147949260000059
Figure RE-GDA0003147949260000061
Step S1.4.2: estimating neural network module MkEstimated variance under codebook record of (1)
Figure RE-GDA0003147949260000062
Figure RE-GDA0003147949260000063
Wherein med (.) is the median calculation;
step S2: real-time multi-step prediction
Step S2.1: initializing the running time ts of the prediction model as t0+ 1;
step S2.2: obtaining chip performance characterization parameter data x recorded by sensorts
Step S2.3: judging whether the prediction program is manually stopped or whether the running time ts reaches the set upper limit value tmaxIf yes, ending the real-time prediction program; if not, the step S2.4 is carried out: (ii) a
Step S2.4: updating a prediction model and generating real-time prediction data
As shown in fig. 2, this step includes:
step S2.4.1: initializing the prediction step number k to be 1;
step S2.4.2: judging whether the predicted step number K is larger than the maximum predicted step number K, if so, entering step S2.5, and if not, entering step S2.4.3;
step S2.4.3 generating neural network module MkInput vector of (2):
Figure RE-GDA0003147949260000064
wherein for data at time t
Figure RE-GDA0003147949260000065
The values are:
Figure RE-GDA0003147949260000066
step S2.4.4 calculating k prediction data
Figure RE-GDA0003147949260000067
Step S2.4.5 updating the prediction inputs of the prediction module
Figure RE-GDA0003147949260000068
True output
Figure RE-GDA0003147949260000069
And desired output
Figure RE-GDA00031479492600000610
Step S2.4.6 updating codebook information
Step S2.4.6.1 statistical neural network Module MkCode book under prediction error of
Figure RE-GDA00031479492600000611
Figure RE-GDA00031479492600000612
Step S2.4.6.2 estimating neural network Module MkEstimated variance under codebook record of (1)
Figure RE-GDA00031479492600000613
Figure RE-GDA00031479492600000614
Wherein med (.) is the median calculation;
step S2.4.7, update the output weight of the prediction module
Figure RE-GDA00031479492600000615
Wherein ΛkFor the correlation entropy diagonal matrix:
Figure RE-GDA0003147949260000071
step S2.4.8, updating k to k +1, and returning to step S2.4.2;
s2.5, obtaining K-step prediction data of the running time ts
Figure RE-GDA0003147949260000072
And (5) updating ts to ts +1, returning to the step S2.2, and predicting the K-step prediction data of the next operation time in real time.
Examples of the invention
To illustrate the technical effects of the present invention, the present invention is now verified by taking the prediction of the accelerated degradation saturation voltage drop of the insulated gate bipolar transistor as an example. The accelerated degradation saturation voltage drop of the insulated gate bipolar transistor can effectively reflect the health state of the device. In order to verify the effectiveness of the method, the implemented saturation voltage drop of the insulated gate bipolar transistor under the accelerated degradation experiment is predicted through the prediction model established by the method.
Meanwhile, the online prediction precision is shown in table 1 by comparing the method with a gated cyclic unit network (GRU), a long and short memory network (LSTM), an Extreme Learning Machine (ELM) and a cyclic extreme learning machine (RNN-ELM).
Figure RE-GDA0003147949260000073
TABLE 1
As can be seen from Table 1, the method of the present invention has higher online prediction accuracy compared to the existing method, and the multi-step prediction result is more accurate compared to the existing method.
Although illustrative embodiments of the present invention have been described above to facilitate the understanding of the present invention by those skilled in the art, it should be understood that the present invention is not limited to the scope of the embodiments, and various changes may be made apparent to those skilled in the art as long as they are within the spirit and scope of the present invention as defined and defined by the appended claims, and all matters of the invention which utilize the inventive concepts are protected.

Claims (1)

1. A chip performance degradation trend prediction method based on a multi-step robust prediction learning machine is characterized by comprising the following steps:
(1) initializing the prediction model
1.1), setting the maximum prediction step number K, and importing training historical data Xhisto＝{x1,x2,…,xt0In which xtThe chip performance characterization parameter data recorded by the sensor at the t-th moment is t 1,2, …, t0 and t0, wherein t is the number of historical data;
structure of initializing recurrent neural network M ═ M1,M2,M3,…,MK}，Mk＝{Xk,Whk,βk,Ek,σkThe neural network module for the k prediction, where XkFor neural network module MkInput of (1), WhkFor neural network module MkHidden layer weight of (1), betakFor neural network module MkOutput layer weight of EkFor neural network module MkOf the code book, sigmakFor neural network module MkThe estimated variance under the codebook record of (1);
obtaining optimal prediction dimension m and delay coefficient t of model input sample by utilizing optimal entropy rate methodd；
Initializing WhkIs a two-dimensional random matrix of m × N, betakIs an N-dimensional zero vector;
1.2), calculating the k step neural network module MkInput vector X ofk：
Figure FDA0003503685070000011
Figure FDA0003503685070000012
Setting a k step neural network module MkThe true outputs of (c) are:
Figure FDA0003503685070000017
1.3) generating a recurrent neural network under offline training as a prediction model
1.3.1), compute hidden layer input Hk：
Figure FDA0003503685070000013
Wherein the content of the first and second substances,
Figure FDA0003503685070000014
is the k step neural network module MkT 1,2, …, t0-k-mtd：
Figure FDA0003503685070000015
Wherein f (.) is a Sigmoid activation function;
1.3.2) generating weights β for output layersk：
Figure FDA0003503685070000016
1.3.3), calculating prediction output
Figure FDA0003503685070000021
1.4) generating an error code book in an off-line training state
1.4.1), statistical neural network module MkCode book under prediction error of
Figure FDA0003503685070000022
1.4.2), estimating neural network module MkEstimated variance under codebook record of (1)
Figure FDA0003503685070000023
Figure FDA0003503685070000024
Wherein med (.) is the median calculation;
(2) real-time multi-step prediction
2.1) initializing the running time ts of the prediction model to be t0+ 1;
2.2) obtaining chip performance characterization parameter data x recorded by the sensorts
2.3), judging whether the prediction program is manually stopped or whether the running time ts reaches the set upper limit value tmaxIf yes, ending the real-time prediction program; if not, entering the step 2.4);
2.4), updating the prediction model and generating real-time prediction data
2.4.1), initializing the prediction step number k to be 1;
2.4.2), judging whether the predicted step number K is larger than the maximum predicted step number K, if so, entering the step 2.5), and if not, entering the step 2.4.3);
2.4.3), generating neural network module MkInput vector of (2):
Figure FDA0003503685070000025
wherein for data at time t
Figure FDA0003503685070000026
The values are:
Figure FDA0003503685070000027
2.4.4), computing k-step prediction data
Figure FDA0003503685070000028
2.4.5), updating prediction inputs of prediction module
Figure FDA0003503685070000029
True output
Figure FDA00035036850700000210
And desired output
Figure FDA00035036850700000211
2.4.6), updating codebook information
2.4.6.1), statistical neural network module MkCode book under prediction error of
Figure FDA00035036850700000212
2.4.6.2), estimating neural network module MkEstimated variance under codebook record of (1)
Figure FDA00035036850700000213
Figure FDA00035036850700000214
Wherein med (.) is the median calculation;
2.4.7), update prediction module output weights
Figure FDA00035036850700000215
Wherein ΛkFor the correlation entropy diagonal matrix:
Figure FDA00035036850700000216
2.4.8), update k ═ k +1, return to step 2.4.2);
2.5) obtaining K-step prediction data of operation time ts
Figure FDA0003503685070000031
And (5) updating ts to be ts +1, and returning to the step 2.2), and predicting the K-step prediction data of the next operation time in real time.
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