CN116187383A - Method for determining 3D NoC optimal test plan based on improved whale optimization algorithm - Google Patents
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Abstract
The invention belongs to the technical field of 3DNoC test application, and particularly relates to a method for determining 3DNoC optimal test plan based on an improved whale optimization algorithm. The invention provides a method for determining 3DNoC optimal test plan based on an improved whale optimization algorithm, which is characterized in that the improved whale optimization algorithm is applied to the 3DNoCIP core test plan, so that the IP core test data distribution and scheduling sequence is used as a variable to finish iterative optimization by using the algorithm, the optimal IP core test sequence is found out, and the shortest test time reaches the purpose of minimizing test time and test power consumption, thereby solving the problem of finding the existing optimal solution.
Description
Technical Field
The invention belongs to the technical field of 3D NoC test application, and particularly relates to a method for determining 3D NoC optimal test plan based on an improved whale optimization algorithm.
Background
With the continued development of three-dimensional integrated circuit technology, three-dimensional network on chip (three dimensional network-on-chip,3D NoC) has evolved. However, with the continuous increase in the number and complexity of embedded cores in a system, it is a significant challenge to increase the testing efficiency of the system, requiring the rational distribution of the test data of the cores under test, the effective utilization of the channel bandwidth and the number and location of the test interfaces. How to maximize the efficiency of testing under limited resources becomes an NP-hard problem. It has been reported that nocs have had their cost of manufacture exceeded, and that the time spent testing soft costs of the cost of testing accounts for 80% -90% of the total cost of testing. Therefore, the improvement of the test efficiency and the reduction of the test cost play a role in the improvement of the yield in the chip manufacturing process, and have high research value.
At present, most of the test researches on the network-on-chip adopt group intelligent optimization algorithms which are relatively not ideal, and aiming at the problem of parallel testing of the resource cores in the 3D NoC, test planning aims at reasonably distributing test data of the IP cores and arranging test sequences of the IP cores. When the improved whale optimization algorithm is used for optimizing the 3D NoC test planning scheme, the test scheduling scheme is organically mapped into the algorithm, so that the test planning problem is coded so that the algorithm can be effectively solved.
As the scale of integrated circuits increases, the number of integrated IP cores in the system increases, and how to find the best solution for 3D NoC test becomes the direction of the current intensive research.
Disclosure of Invention
Aiming at the problem of determining the optimal solution of the 3D NoC test, the invention provides a method for determining the 3DNoC optimal test plan based on an improved whale optimization algorithm, which is simple in method, convenient to operate and capable of effectively determining the optimal solution of the 3D NoC test.
In order to achieve the above purpose, the technical scheme adopted by the invention is that the invention provides a method for determining 3DNoC optimal test plan based on improved whale optimization algorithm, which comprises the following steps:
a. firstly, each node of a 3DMesh topological structure is encoded, an IP core is mapped into the topological structure, then a plurality of I/O ports are selected from the topological structure, test vectors of each IP core are transmitted into a network from an input port, then each IP core is tested, wherein test data of the IP core is distributed into a matrix X i The following are provided:
X i ＝[x i1 ,x i2 ,...,x in ]
wherein: x is x ij Test vectors representing IP core j in the ith test data allocation scheme are allocated to the xth test data allocation scheme ij Transmitting on a strip TAM, wherein j is not less than 1 and not more than n, x is not less than 1 ij ≤B；
The test sequence matrix S for the IP core is as follows:
wherein: s is S bj The core representing the label is tested j on item B TAM, where 1.ltoreq.b.ltoreq.b, 1.ltoreq.j.ltoreq.n;
b. then, initializing whale population by using a chaotic sequence, distributing test data to each TAM, recording the generated SN distribution schemes in an X matrix, setting the maximum allowable total power consumption and layer power consumption of the system, and calculating related parameters: initializing a food source population by using a chaotic sequence according to a selected coding matrix of the TAM of IP core test data to form a first generation population, judging whether each individual is effective or not, and if the individuals are unqualified, re-initializing;
c. adopting a multiplexing 3D NoC as a TAM mode, and adopting a parallel test mode to transmit test vectors of the IP cores so as to test the IP cores;
d. and testing the IP core according to the allocation scheme, and obtaining the fitness value f (x). Comparing the fitness value of each target prey and the test planning result, and obtaining self-optimal pbest and global optimal gbest through comparison;
e. for each food source, if f (Xi) < f (Xpbest), xpbest=xi, and comparing to obtain an individual extremum pbest; if f (Xpbest) < f (Xgbest), xgbest=xpbest, comparing the extremum of all individuals to obtain global extremum gbest;
f. the whale individual adopts a formula to update the position, when the whale kills the target hunting object, the whale is selected to shrink the surrounding or spiral update mechanism to update the position through 50% probability, the position of the target hunting object is obtained, then the fitness value of the new target hunting object and the old target hunting object is compared, and the better target hunting object is added into the population to obtain a global optimal solution;
g. b, judging whether the maximum iteration times of the population are reached, if not, turning to the step b, otherwise, outputting the optimal test data distribution scheme;
h. optimizing the test sequence of the IP core to be tested on each TAM on the basis of the optimal test data distribution scheme of the IP core, initializing a dispatching sequence population by using a chaotic sequence according to the test sequence coding matrix of the IP core, checking the validity of the dispatching sequence population, performing parallel test on the IP core, updating relevant test information to obtain an adaptability value of the dispatching sequence of each target hunting object, and performing population iterative evolution based on the evolution strategy of the algorithm;
i. and judging whether the maximum iteration times of the population are reached, if not, turning to the step f, continuing to perform iterative optimization, otherwise, ending the algorithm, and outputting a test planning scheme with the minimum test time.
Preferably, in the step b, the expression of the Tent chaotic map for initializing the whale population by using the chaotic sequence is as follows:
wherein the method comprises the steps of
For a chaotic sequence, k=1, 2,..d, D is the initialized population dimension, i=1, 2,..sn, SN is the initialized population number. The chaotic value can be mapped into the searching space of the population by utilizing the Tent chaotic mapping, namely
In (1) the->
For the position of individual i in dimension k, [ ub ] i -lb i ]To search for boundaries.
Preferably, in the step d, the reciprocal of the test time of all the resource cores in the system, that is, the reciprocal of the objective function value, is selected as the fitness value.
Preferably, in the step f, inertial weights are introduced into the whale optimization algorithm to balance global searching capability and local development capability of the algorithm, and the inertial weights are generated in an adaptive nonlinear mode, wherein the calculation formula is as follows:
where t is the number of iterations, ω max Is the maximum value of the inertia weight, and thus the updated model of the individual position can be obtained as follows:
in the early stage of iteration, the global optimal solution has weak gravitation to the individual moving direction, and the population can perform extensive global search to enhance the searching capability; in the later stages of the iteration, the inertia weight approaches 0.
Compared with the prior art, the invention has the advantages and positive effects that,
1. the invention provides a method for determining a 3D NoC optimal test plan based on an improved whale optimization algorithm, which is characterized in that the improved whale optimization algorithm is applied to the 3D NoC IP core test plan, so that the IP core test data distribution and scheduling sequence is used as a variable to finish iterative optimization by using the algorithm, the optimal IP core test sequence is found out, and the shortest test time reaches the purpose of minimizing test time and test power consumption, thereby solving the problem of finding the existing optimal solution.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the description of the embodiments will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort to a person skilled in the art.
FIG. 1 is a flowchart of the whale optimization algorithm provided in this example 1;
FIG. 2 is a flow chart of the 3D NoC test schedule provided in this example 1 based on the modified ABC algorithm;
fig. 3 is a schematic diagram of a 3D NoC IP core parallel test data transmission path provided in embodiment 1;
fig. 4 is a timing chart for providing IP core parallel test in embodiment 1.
Detailed Description
In order that the above objects, features and advantages of the invention will be more clearly understood, a further description of the invention will be rendered by reference to the appended drawings and examples. It should be noted that, in the case of no conflict, the embodiments of the present application and the features in the embodiments may be combined with each other.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, however, the present invention may be practiced otherwise than as described herein, and therefore the present invention is not limited to the specific embodiments of the disclosure that follow.
Embodiment 1, as shown in FIGS. 1-4, the present embodiment provides a method for determining a 3D NoC optimal test plan based on an improved whale optimization algorithm
Firstly, each node of a 3D Mesh topological structure is encoded, IP cores are mapped into the topological structure, then a plurality of I/O ports are selected from the topological structure, test vectors of each IP core are transmitted into a network from an input port, and each IP core is tested. The invention is explained by taking a 3D Mesh topological structure of 3 multiplied by 3 as an example, and the specific steps are as follows:
1. the IP core in the circuit with the representativeness in the international test reference circuit ITC'02 is selected as the IP core to be tested, and the parameters (the number and the positions of the input and output ports, the number of test vectors, the test power consumption, the test time and the like of each IP core are initialized
2. Mapping the IP core into the topological structure, and the mapping method comprises the following steps: and balancing the test time and the test power consumption of each layer in the 3D Mesh structure.
3. The test vector enters the topological structure from the input port, and the parallel test is carried out on a plurality of IP cores under the constraint conditions of meeting certain bandwidth, power consumption and the like.
4. The test planning problem is coded so that the whale optimization algorithm can be effectively solved.
In order to organically integrate the 3D NoC IP core test planning model with the improved whale optimization algorithm, some relevant algorithm models are now defined.
(1) Target prey location: the location of each target prey represents one test scheduling scheme that completes all IP core tests, i.e., an individual.
(2) Population: one population is composed of a plurality of individuals, and one population contains a plurality of test scheduling schemes, namely SN target prey positions, and the size of the population represents the size of the population.
(3) Fitness value: and selecting the reciprocal of the test time of all the resource cores in the system, namely the reciprocal of the objective function value, as the fitness value, so as to judge the quality of the objective hunting object.
2. Coding scheme: let the scale of 3D NoC be MxNxL, there are B TAMs to test in parallel, N cores of resource to be tested.
Test data distribution matrix X for IP cores i The following are provided:
X i ＝[x i1 ,x i2 ,...,x in ]
wherein: x is x ij Representation ofTest vectors of IP core j in ith test data allocation scheme are allocated to the xth ij Transmitting on a strip TAM, wherein j is not less than 1 and not more than n, x is not less than 1 ij ≤B。
The test sequence matrix S for the ip core is as follows:
wherein: s is S bj The core representing the label is tested at item B TAM for item j, where 1.ltoreq.b.ltoreq.b, 1.ltoreq.j.ltoreq.n.
The traditional whale optimization algorithm generates initial population positions by a random function, but the initial population distribution uniformity and the ergodic performance obtained in this way are insufficient, the searching efficiency of the algorithm is reduced, and the following improvement is made for this purpose:
(1) Population initialization based on chaotic Tent mapping: the initial population of the algorithm is generated by utilizing the nonlinear dynamic characteristics of ergodic property, randomness, regularity and the like of the chaotic system, so that the randomness of the original algorithm and the diversity of the initial population are ensured, and the chaotic system also has better ergodic property in a search interval. The chaotic mapping can improve the diversity of the initial population, and in a common chaotic system, the Logistic chaotic value is too dense at the boundary of a region, the uniformity is poor, and the diversity of the initial population is influenced. Compared with the Logistic chaos value, the Tent chaos value is more uniform, the distribution in the search interval is more uniform, and the diversity requirement of the initial population can be met. Thus, we can refine the initial population through Tent chaotic mapping. The expression of the Tent chaotic map is
Wherein the method comprises the steps of
For a chaotic sequence, k=1, 2,..d, D is the initialized population dimension, i=1, 2,..sn, SN is the initialized population number. The chaotic value can be mapped into the searching space of the population by utilizing the Tent chaotic mapping, namely
In (1) the->
For the position of individual i in dimension k, [ ub ] i -lb i ]To search for boundaries.
Adaptive inertial weights: for the whale optimization algorithm, the population gradually approaches to the globally optimal solution as the number of iterations increases. If the current global optimal solution is not the true optimal solution, the algorithm enters a premature convergence state. To avoid this, inertial weights are introduced into the whale optimization algorithm to balance the global search and local development capabilities of the algorithm. Generating inertial weights in an adaptive nonlinear mode, wherein the calculation formula is as follows:
where t is the number of iterations, ω max Is the maximum value of the inertia weight, and thus the updated model of the individual position can be obtained as follows:
in the early stage of iteration, the global optimal solution has weak gravitation to the individual moving direction, and the population can perform extensive global search to enhance the searching capability; at the later stage of iteration, the inertia weight approaches 0, and the local development capability of the algorithm can be improved.
The algorithm realizes the specific flow:
(1) Initializing a population: initializing whale population by using a chaotic sequence, namely distributing test data to each TAM, recording the generated SN distribution schemes in an X matrix, setting the maximum allowable total power consumption and layer power consumption of the system, and calculating related parameters: population scale SN, population dimension D, algorithm maximum iteration number MCN, parameters contained in the algorithm, and the like. And initializing a food source (bee) population by using a chaotic sequence according to the selection coding matrix of the TAM by the IP nuclear test data to form a first-generation population. And judging whether each individual is effective, and if the individual is unqualified, re-initializing is needed.
(2) Parallel testing is carried out on the IP core: and adopting a multiplexing 3DNoC as a TAM mode, and adopting a parallel test mode to transmit the test vector of the IP core so as to test the IP.
(3) And (5) calculating a fitness value: and testing the IP core according to the allocation scheme, and obtaining the fitness value f (x). And comparing the fitness value of each target prey and the test planning result to obtain the self optimal pbest and the global optimal gbest.
(4) Updating the individual extremum and the global extremum: for each food source, if f (Xi) < f (Xpbest), xpbest=xi, and comparing to obtain an individual extremum pbest; if f (Xpbest) < f (Xgbest), xgbest=xpbest, the extremum of all individuals is compared, yielding a global extremum gbest.
(5) Population updating: the whale individual adopts a formula to update the position, when the whale kills the target hunting object, the whale is selected to shrink the surrounding or spiral update mechanism to update the position through 50% probability, the target hunting object position is obtained, then the fitness value of the new target hunting object and the old target hunting object is compared, and the better target hunting object is added into the population, so that the global optimal solution is obtained.
(6) Judging whether the maximum iteration times of the population are reached, if not, turning to the step (1), otherwise, outputting the optimal test data allocation scheme.
(7) And optimizing the IP core test scheduling sequence: based on the optimal test data distribution scheme of the IP cores, optimizing the test sequence of the IP cores to be tested on each TAM, initializing a dispatching sequence population by using a chaotic sequence according to the test sequence coding matrix of the IP cores, checking the validity of the dispatching sequence population, performing parallel test on the IP cores, updating relevant test information to obtain the adaptability value of the dispatching sequence of each target hunting object, and performing population iterative evolution based on the evolution strategy of the algorithm.
(8) Judging whether the maximum iteration times of the population are reached, if not, turning to the step (5) to continue iteration optimization, otherwise, ending the algorithm, and outputting a test planning scheme with the minimum test time.
The present invention is not limited to the above-mentioned embodiments, and any equivalent embodiments which can be changed or modified by the technical content disclosed above can be applied to other fields, but any simple modification, equivalent changes and modification made to the above-mentioned embodiments according to the technical substance of the present invention without departing from the technical content of the present invention still belong to the protection scope of the technical solution of the present invention.
Claims (4)
1. A method for determining a 3DNoC optimal test plan based on an improved whale optimization algorithm, comprising the steps of:
a. firstly, each node of a 3DMesh topological structure is encoded, an IP core is mapped into the topological structure, then a plurality of I/O ports are selected from the topological structure, test vectors of each IP core are transmitted into a network from an input port, then each IP core is tested, wherein test data of the IP core is distributed into a matrix X i The following are provided:
X i ＝[x i1 ,x i2 ,...,x in ]
wherein: x is x ij Test vectors representing IP core j in the ith test data allocation scheme are allocated to the xth test data allocation scheme ij Transmitting on a strip TAM, wherein j is not less than 1 and not more than n, x is not less than 1 ij ≤B；
The test sequence matrix S for the IP core is as follows:
wherein: s is S bj The core representing the label is tested j on item B TAM, where 1.ltoreq.b.ltoreq.b, 1.ltoreq.j.ltoreq.n;
b. then, initializing whale population by using a chaotic sequence, distributing test data to each TAM, recording the generated SN distribution schemes in an X matrix, setting the maximum allowable total power consumption and layer power consumption of the system, and calculating related parameters: initializing a food source population by using a chaotic sequence according to a selected coding matrix of the TAM of IP core test data to form a first generation population, judging whether each individual is effective or not, and if the individuals are unqualified, re-initializing;
c. adopting a multiplexing 3DNoC as a TAM mode, and adopting a parallel test mode to transmit test vectors of the IP core so as to test the IP;
d. and testing the IP core according to the allocation scheme, and obtaining the fitness value f (x). Comparing the fitness value of each target prey and the test planning result, and obtaining self-optimal pbest and global optimal gbest through comparison;
e. for each food source, if f (Xi) < f (Xpbest), xpbest=xi, and comparing to obtain an individual extremum pbest; if f (Xpbest) < f (Xgbest), xgbest=xpbest, comparing the extremum of all individuals to obtain global extremum gbest;
f. the whale individual adopts a formula to update the position, when the whale kills the target hunting object, the whale is selected to shrink the surrounding or spiral update mechanism to update the position through 50% probability, the position of the target hunting object is obtained, then the fitness value of the new target hunting object and the old target hunting object is compared, and the better target hunting object is added into the population to obtain a global optimal solution;
g. b, judging whether the maximum iteration times of the population are reached, if not, turning to the step b, otherwise, outputting the optimal test data distribution scheme;
h. optimizing the test sequence of the IP core to be tested on each TAM on the basis of the optimal test data distribution scheme of the IP core, initializing a dispatching sequence population by using a chaotic sequence according to the test sequence coding matrix of the IP core, checking the validity of the dispatching sequence population, performing parallel test on the IP core, updating relevant test information to obtain an adaptability value of the dispatching sequence of each target hunting object, and performing population iterative evolution based on the evolution strategy of the algorithm;
i. and judging whether the maximum iteration times of the population are reached, if not, turning to the step f, continuing to perform iterative optimization, otherwise, ending the algorithm, and outputting a test planning scheme with the minimum test time.
2. The method for determining a 3D NoC optimal test plan based on an improved whale optimization algorithm according to claim 1, wherein in step b, the expression of the Tent chaotic map for initializing whale population using a chaotic sequence is:
wherein the method comprises the steps of
For a chaotic sequence, k=1, 2,..d, D is the initialized population dimension, i=1, 2,..sn, SN is the initialized population number. The chaotic value can be mapped into the searching space of the population by utilizing the Tent chaotic mapping, namely
In (1) the->
For the position of individual i in dimension k, [ ub ] i -lb i ]To search for boundaries.
3. The method for determining a 3D NoC optimal test plan based on an improved whale optimization algorithm according to claim 2, wherein in step D, the reciprocal of the time for all resource kernels in the system to complete the test, i.e. the reciprocal of the objective function value, is selected as the fitness value.
4. The method for determining 3D NoC optimal test plan based on improved whale optimization algorithm according to claim 3, wherein in step f, inertial weights are introduced into the whale optimization algorithm to balance global searching capability and local development capability of the algorithm, and inertial weights are generated in an adaptive nonlinear manner, wherein the calculation formula is as follows:
where t is the number of iterations, ω max Is the maximum value of the inertia weight, and thus the updated model of the individual position can be obtained as follows:
in the early stage of iteration, the global optimal solution has weak gravitation to the individual moving direction, and the population can perform extensive global search to enhance the searching capability; in the later stages of the iteration, the inertia weight approaches 0.
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