Embryonic electronic cell array, electronic cell granularity, preferred methods and devices

Abstract

The embodiment of the specification provides an embryo electronic cell array electronic cell granularity optimization method and device, wherein the method comprises the following steps: acquiring an electronic cell granularity parameter, and establishing an embryo electronic cell array reliability analysis model and an intracellular auxiliary resource proportion analysis model based on the electronic cell granularity parameter; based on the embryo electronic cell array reliability analysis model and the intracellular auxiliary resource proportion analysis model, an electronic cell granularity optimization model is established with the maximum embryo electronic cell array reliability as the target; a self-adaptive immune operator is determined according to the algorithm iteration number and the population quality, so as to determine an improved self-adaptive artificial immune algorithm, and the improved self-adaptive artificial immune algorithm is used to realize the solution of the embryo electronic cell array electronic cell granularity optimization according to the electronic cell granularity optimization model.

Description

Technical Field

[0001] This document relates to the field of biomimetic self-repair technology, and in particular to a method and apparatus for optimizing the particle size of an embryonic electronic cell array. Background Technology

[0002] Embryonic electronic cell arrays are a novel type of biomimetic hardware designed to mimic the growth and development of multicellular organisms. They possess self-detection and self-repair capabilities, effectively improving fault tolerance and environmental adaptability. They hold broad application prospects in fields with complex working environments, where manual maintenance is difficult, and where stringent reliability requirements necessitate long-term continuous and reliable operation of electronic equipment, such as aerospace, deep-sea exploration, and battlefield environments.

[0003] Currently, research on embryonic electronic cell arrays mainly focuses on array structure design and self-repair strategies, fault self-detection methods, array reliability assessment methods, and array optimization design. Among these, the optimization design of embryonic electronic cell arrays primarily includes the selection of row and column numbers, research on fault self-repair strategies, array layout methods, optimal selection of the number of electronic cells, and research on the number of intracellular gene backups.

[0004] In the design of embryonic electronic cell arrays, for functional circuits of different sizes, variations in the size of the electronic cells directly affect the number of rows and columns available for self-repair within the array, thus impacting the array's reliability. Furthermore, in the redesign of existing functional circuits based on embryonic electronic cell arrays, the selection of electronic cell size relies primarily on the designer's experience, lacking theoretical guidance. Summary of the Invention

[0005] The purpose of this invention is to provide a method and apparatus for optimizing the particle size of embryonic electronic cell arrays, thereby solving the aforementioned problems in the prior art.

[0006] This invention provides a method for optimizing the particle size of an embryonic electronic cell array, comprising: Obtain the electron cell particle size parameters, and establish an embryonic electron cell array reliability analysis model and an intracellular auxiliary resource ratio analysis model based on the electron cell particle size parameters; Based on the aforementioned embryonic electronic cell array reliability analysis model and intracellular auxiliary resource ratio analysis model, an electronic cell granularity optimization model is established with the goal of maximizing the reliability of the embryonic electronic cell array. The adaptive immune operator is determined based on the number of algorithm iterations and population quality, thereby determining the improved adaptive artificial immune algorithm. Based on the described electronic cell granularity optimization model, the improved adaptive artificial immune algorithm is used to solve the problem of optimizing the electronic cell granularity of the embryonic electronic cell array.

[0007] This invention provides an apparatus for optimizing the particle size of an embryonic electronic cell array, comprising: The first module is used to acquire electron cell particle size parameters and establish an embryonic electron cell array reliability analysis model and an intracellular auxiliary resource ratio analysis model based on the electron cell particle size parameters. The second module is used to establish an electronic cell granularity optimization model based on the embryonic electronic cell array reliability analysis model and the intracellular auxiliary resource ratio analysis model, with the goal of maximizing the reliability of the embryonic electronic cell array. The computation module is used to determine the adaptive immune operator based on the number of algorithm iterations and population quality, thereby determining the improved adaptive artificial immune algorithm. Based on the electronic cell granularity optimization model, the improved adaptive artificial immune algorithm is used to solve the problem of optimizing the electronic cell granularity of the embryonic electronic cell array.

[0008] This invention also provides an embryonic electronic cell array electronic cell particle size optimization device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor. When the computer program is executed by the processor, it implements the steps of the above-described embryonic electronic cell array electronic cell particle size optimization method.

[0009] This invention also provides a computer-readable storage medium storing an information transmission implementation program, which, when executed by a processor, implements the steps of the above-described method for optimizing the electronic cell particle size of an embryonic electronic cell array.

[0010] By employing embodiments of the present invention, during the functional circuit design process, an improved adaptive artificial immune algorithm is used to solve the problem of optimal electron cell granularity, thereby obtaining the optimal electron cell granularity that adapts to the circuit scale, effectively improving the reliability of the circuit, and also providing theoretical guidance for the optimized design of embryonic electron cell array applications. Attached Figure Description

[0011] To more clearly illustrate the technical solutions in one or more embodiments of this specification or in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this specification. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0012] Figure 1 This is a flowchart of the method for optimizing the particle size of embryonic electronic cell arrays according to an embodiment of the present invention; Figure 2 This is a schematic diagram illustrating the variation of the proportion of auxiliary resources within an electronic cell with cell size according to an embodiment of the present invention. Figure 3 This is a schematic diagram illustrating the optimization process of the electronic cell granularity of the six functional circuits in this invention under two optimization algorithms; Figure 4 This is a schematic diagram of an embryonic electronic cell array electronic cell particle size optimization device according to one embodiment of the present invention; Figure 5 This is a schematic diagram of the embryonic electronic cell array electronic cell particle size optimization device in Embodiment 2 of the present invention. Detailed Implementation

[0013] The technical problem to be solved by this invention is to address the shortcomings of the existing technology. Taking two-dimensional embryonic electronic cell arrays as the research object, this invention proposes an electronic cell granularity optimization method based on an adaptive artificial immune algorithm in the design process of embryonic electronic cell arrays. This method can be extended to the design of other types of arrays.

[0014] To enable those skilled in the art to better understand the technical solutions in one or more embodiments of this specification, the technical solutions in one or more embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this specification, and not all of the embodiments. Based on one or more embodiments of this specification, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of this document.

[0015] Method Implementation Examples According to embodiments of the present invention, a method for optimizing the particle size of embryonic electronic cell arrays is provided. Figure 1 This is a flowchart of the method for optimizing the particle size of embryonic electronic cell arrays according to an embodiment of the present invention, such as... Figure 1 As shown, the method for optimizing the particle size of embryonic electronic cell arrays according to an embodiment of the present invention specifically includes: Step 101: Obtain the electron cell particle size parameters, and establish an embryonic electron cell array reliability analysis model and an intracellular auxiliary resource ratio analysis model based on the electron cell particle size parameters; Step 102: Based on the embryonic electronic cell array reliability analysis model and the intracellular auxiliary resource ratio analysis model, an electronic cell particle size optimization model is established with the goal of maximizing the reliability of the embryonic electronic cell array. Step 103: Determine the adaptive immune operator based on the number of algorithm iterations and population quality, thereby determining the improved adaptive artificial immune algorithm. Based on the electronic cell granularity optimization model, use the improved adaptive artificial immune algorithm to solve the problem of optimizing the electronic cell granularity of the embryonic electronic cell array.

[0016] In summary, this invention introduces an electron cell granularity parameter to establish a reliability analysis model for embryonic electron cell arrays and an intracellular auxiliary resource ratio analysis model. Secondly, with maximizing the reliability of the embryonic electron cell array as the design objective, an electron cell granularity optimization model is established. Then, based on the algorithm iteration count and population quality, an adaptive immune operator is designed, and an improved adaptive artificial immune algorithm is proposed to solve the electron cell granularity optimization problem. Finally, experiments on electron cell granularity selection for embryonic electron cell arrays of various sizes are conducted to verify the effectiveness and rationality of the electron cell granularity optimization method. The following provides a detailed description of each step in this invention.

[0017] Step 1: Calculate the reliability of the embryonic electronic cell array after introducing electronic cell granularity.

[0018] Within an embryonic electron cell array, the granularity of an electron cell represents the scale at which it can perform logical functions. However, the logical functions of a circuit are difficult to describe quantitatively. To study the impact of changes in electron cell granularity on array reliability, the area of ​​the electron cells is used to characterize their granularity. Introducing the electron cell granularity parameter means that changes in granularity directly cause changes in the electron cell area; therefore, the area of ​​the array is represented by the product of the number of electron cells and their granularity.

[0019] In the application design of embryonic electronic cell arrays, changes in electron cell particle size directly affect the array's reliability. To facilitate the study of the impact of electron cell particle size variations on array reliability, we assume the electron cell particle size is g, with an initial value of 1. If g increases by 10%, then g = 1.1; if g decreases by 10%, then g = 0.9. The ratio of intracellular auxiliary resource area to the total cell area is α. (0, 1), α = 0 indicates no auxiliary resources within the cell. Let g = 1, α = 0, and the area of ​​the electron cell be S0, and the array of working electron cells in the array be m. n, m, and n' represent the number of rows and columns of cells in the working array, respectively, and the size of the array is M. N, M, and N represent the number of rows and columns of cells in the array, respectively, and the failure rate of the cell unit is λ.

[0020] The auxiliary resource ratio is the ratio of auxiliary resources consumed within a cell to the hardware resources consumed by the electron cells. For a given functional circuit, during circuit design, the array area and the total area of ​​all working cells within the array remain constant. In this case, changes in the electron cell granularity will cause changes in the number of cells within the array. Smaller electron cell granularity results in more electron cells in the array, allowing for more self-repair cycles and higher array reliability. However, a larger number of electron cells also increases the complexity of fault detection, repair control logic, and wiring within the cells, leading to an increase in the area of ​​auxiliary resources within the cells. This increased auxiliary resource ratio will result in a decrease in the number of electron cells within the array, reducing the number of self-repair cycles and causing a decrease in array reliability.

[0021] Within an embryonic electronic cell array, after introducing two parameters—cell particle size g and the proportion of intracellular auxiliary resources α—the area S of the cell unit... g Given gS0 / (1 - α), the number of rows m of the working electron cell array. g = , number of columns ng = The number of rows M of the embryonic electronic cell array g = Number of columns N g = In the formula This indicates a floor operation, where the failure rate λ of the electron cell is... g = gλ / (1 - α). The reliability R of the array. g (t) is (1) In the formula: p(t) is the probability that an electron cell row can function normally, and p(t) is (2) The MTTF of the embryonic electronic cell array is T g Represented as (3) Step 2: Analysis of the impact of cell particle size variation on cell resource consumption Currently, embryonic electron cells lack a standardized structure. To study the optimal electron cell size, the electron cells within the embryonic electron cell array are determined as the baseline electron cell size. At this point, the cell size g = 1, and the proportion of intracellular auxiliary resources is α0. Under the baseline cell size, the size of the embryonic electron cell array is M0. N0, the size of the working array is m0 n0, where m0 = m, n0 = n, M0 = N0= .

[0022] Within the embryonic electron cell array, the impact of electron cell particle size variation on intracellular accessory resources is mainly reflected in the following three aspects: Firstly, the width of the address generator. The smaller the electron cell granularity, the more electron cells in the array, and the wider the address generator; conversely, the larger the granularity, the narrower the address generator. After the cell granularity changes, the row address width of the array changes from... log2M0 Change to log2M g The column address width of the array is determined by log2N0 Become log2N g ,in This is a rounding up operation.

[0023] Secondly, regarding the auxiliary wiring circuitry of the I / O module, within the row removal self-healing array, the array's self-healing process does not add additional auxiliary connections as the number of idle cells increases. Within the cell removal self-healing array, each additional idle cell requires four additional connections per cell: two on each side of the cell, one connecting to the cell above it and one connecting to the cell below it. Auxiliary wiring is implemented using a multiplexer, the width of which is determined by the number of additional idle cells.

[0024] After the electron cell granularity changes, the number of bits in the multiplexer of the cell-aided wiring circuit changes from N0-n0 to N. g - n g The width of the data selector control bit is determined by log2(N0 - n0) Change to log2(N g - n g ) The number of MOSFETs consumed by the 4-to-1 data selector in the auxiliary wiring unit is approximately 32. Before the change in electron cell granularity, the number of MOSFETs consumed, Ha, in the auxiliary wiring circuit is approximately: (4) After the electron cell particle size changes, the number of MOSFETs consumed by the auxiliary wiring circuit H ga Approximately: (5) Thirdly, the gene storage configuration module: the smaller the electronic cell particle size, the more cells are in the array, and the more idle cells there are. After the electronic cell particle size changes, the number of genes stored in each cell in the row removal self-repair array changes from 65m0 to... The number of genes stored in each cell of the cell removal self-repair array is determined by... Change to .

[0025] At the baseline electron cell granularity, the hardware resource consumption Hc of a single electron cell within the embryonic electron cell array is approximately: (6) After the cell size changes, the hardware resource consumption of a single electron cell within the embryonic electron cell array... for: (7) Within an embryonic electronic cell array, smaller electronic cell granularity leads to a continuously increasing proportion of intracellular auxiliary resources, and vice versa. The increase in the proportion of auxiliary resources after changes in electronic cell granularity is measured by the ratio of the increase in intracellular auxiliary resources to the consumption of electronic cell hardware resources. In the embryonic electronic cell array, the proportion of auxiliary resources α within the electronic cell after changes in electronic cell granularity is: (8) Step 3: Mathematical Model for Optimizing Electronic Cell Particle Size To study the optimal cell size in the design process of embryonic electronic cell arrays, the electron cells in a typical two-dimensional embryonic electronic cell array are used as the baseline cell size, i.e., g = 1, α = α0, λ0 = λ / (1-α0), where λ is the failure rate of the electron cells when g = 1 and α = 0. For a given designer, their design capability is also determined. A designer's design capability is generally not a fixed value, but a range, i.e., the proportion of intracellular auxiliary resources belongs to the range [α...]. d , α u ], α d and α u All belong to (0, min(1-(n / N)2, 1-(m / M)2), and α d <α u .

[0026] In the application design of embryonic electronic cell arrays, for a given circuit function, if the same designer uses the same design method, the array hardware resource consumption will remain constant. Therefore, the goal of the embryonic electronic cell array application design process is to maximize circuit reliability. After introducing the electronic cell granularity g, let the array's MTTF be T. g (g, α), the optimal model for electron cell particle size within the array is: (9) At this point, the optimal size of electron cells in the embryonic electron cell array becomes a nonlinear programming problem. To facilitate the solution, the constraints need to be processed. The most common method for processing constraints is the penalty function method. After processing with the penalty function method, equation (9) becomes: (10) In the formula: F(g, α) is the fitness function of equation (9); The term is the penalty term, and μ is the penalty coefficient, which is usually a very large positive number.

[0027] Step 4: Improve the adaptive artificial immune algorithm Artificial immune algorithms can effectively solve function optimization problems, while also improving the accuracy and speed of problem solving. However, the algorithm also has drawbacks such as high computational cost and slow convergence speed. Therefore, in artificial immune algorithms, the immune operator is adaptively designed based on parameters such as the number of iterations, antibody activation degree, and average antibody activation degree.

[0028] In artificial immune algorithms, the evolutionary function relies primarily on immune operators, and the quality of these operators directly determines the algorithm's performance. While fixed immune operators can solve optimization problems, they are prone to getting trapped in local optima. During iteration, the performance of the immune algorithm is also affected by the quality of the antibody population and the number of iterations, thus influencing the optimization results. Therefore, to improve the performance of the immune algorithm, an adaptive design of the immune operators is implemented based on the number of iterations and population quality. The specific design of the adaptive immune operators is as follows.

[0029] (1) Adaptive excitation degree calculation operator Antibody activation is a comprehensive indicator for evaluating its quality, requiring consideration of both antibody affinity and concentration. Antibodies with higher affinity and lower concentration exhibit higher activation. Therefore, the antibody activation operator is: (11) In the formula: NP is the size of the antibody population, f aff (A i ) is antibody A i affinity, f den (A i ) is antibody A i concentration, f sim (A i ) is antibody A i The motivation level, β is the affinity weighting coefficient, and (12) In the formula: G is the current iteration number of the algorithm, G max This represents the maximum number of iterations for the algorithm.

[0030] (2) Adaptive immune selection operator The selection operation involves choosing which antibodies from the current population to proceed to the cloning process. To obtain a feasible solution to the problem more quickly, individuals with higher arousal levels should be selected for cloning. Therefore, by calculating the average arousal level of antibodies in the current population and comparing it with the population average, antibodies with higher arousal levels are selected for cloning.

[0031] (13) In the formula: f avg sim(G) represents the average activation level of the Gth generation antibody. Comparison of activation levels f of the Gth generation antibody. sim (A i ) and f avg Based on the relationship between sim(G), the antibody set entering the clonal selection operation in the Gth generation is: (14) In the formula: M(G) is the set of antibodies selected for clonal selection operation in the Gth generation of antibodies.

[0032] (3) Adaptive cloning operator Cloning involves replicating a selected antibody a certain number of times. The most crucial aspect of this process is determining the number of antibody clones to be created. Dynamically determining the number of antibody clones based on antibody quality effectively improves algorithm efficiency. Depending on the excitation level of the selected antibody, the adaptive cloning operator is designed as follows: (15) In the formula: C(A) i ) is antibody A i The number of clones, round(.) is the floor function, w c Here, CL represents the cloning coefficient, and CL represents the population size of the antibody to be cloned. C is a constant greater than 1, ensuring that each antibody has a certain number of clones.

[0033] (4) Adaptive mutation operator Mutation operators are used to mutate the cloned antibody set to generate affinity mutations, enabling local search. By generating potential antibodies through antibody mutation, mutation operators play a crucial role in improving algorithm performance; therefore, the adaptive mutation operator is designed as follows: (16) In the formula: These are mutated antibodies. γ i Let N(0,1) be a random variable in the range [-1,1], and let G(0,1) be a Gaussian variable following a standard normal distribution. Let G be the current iteration algebra. max For the maximum iterative algebra, A max A is the antibody with the highest motivation value in the population. min Let be the antibody with the smallest activation value in the population, and rand be a random variable in the range [0,1].

[0034] (5) Adaptive clonal suppression operator Clonal suppression operators are used for reselection of the clonal population after mutation, selecting antibodies with high affinity to enter the new population and eliminating antibodies with low affinity, thus helping to improve antibody quality. Let the antibodies after cloning and mutation be... The antibody after clonal inhibition is (17) The antibody that enters the next generation after clonal inhibition is A. i (G+1), (18) In the formula: U(0,1) represents a random real number between 0 and 1, p j The rules for determining the value are as follows: If f aff (B i (G)) ≤ f aff (A i (G)), and A i If (G) is the optimal antibody in the current population, then p j = 0; if f aff (B i (G))>f aff (A i (G)), then p j = 1; if f aff (B i (G)) ≤ f aff (A i (G)), and A i If (G) is not the optimal antibody in the current population, then p j for (19) In the formula: γ>0 is a value related to population diversity. Generally, the better the diversity, the larger the value of γ, and vice versa.

[0035] Step 5: Validation of the effectiveness of the electronic cell particle size optimization method To verify the correctness and effectiveness of the proposed method for optimizing the granularity of electron cells within an embryonic electron cell array based on an improved adaptive artificial immune algorithm, six different sizes of embryonic electron cell arrays were selected to conduct experiments to verify the effectiveness of the method.

[0036] Assume the total size of the array remains constant at M. N = 100 100, the failure rate of the electron cell λ = 1 10⁻⁵ / h. Six different scales of functional circuits were selected as experimental objects. The electronic cells in the reference [2] were used as the reference cell size, i.e., g = 1, α = α₀ = 0.3. The scales of the six functional circuits mapped to the embryonic electronic cell array are shown in Table 1. The MTTF of the six scales of embryonic electronic cell arrays are also shown in Table 1.

[0037] Table 1. Array size and MTTF when g = 1, α = 0.3 Table 3 MTTF and scales of 6 kinds of ECA when g = 1, α = 0.3

[0038] The parameters selected in the improved adaptive artificial immune algorithm are as follows: the designer's design capability range is [0.1, 0.6], i.e., α d = 0.1, α u = 0.6, antibody population size NP is 50, maximum number of iterations G max The cloning coefficient is 200, and the cloning coefficient is w. c It is 50. C = 5, clonal inhibition coefficient γ is 1000, penalty coefficient μ 11 = μ 12 = 1 1010, the antibody similarity threshold δ is 0.9.

[0039] 1. Optimal particle size of electron cells within the self-repairing array for row removal. As shown in Table 1, within the six sizes of embryonic electron cell arrays, the increase in the proportion of auxiliary resources within the electron cells as the electron cell particle size decreases from 0.9 to 0.1 is as follows: Figure 2 As shown, Figure 2This study examines the variation of the proportion of auxiliary resources (α) within electron cells in six different sizes of row removal self-repairing embryonic electron cell arrays as the electron cell particle size (g) decreases from 0.9 to 0.1. In the six sizes of row removal self-repairing embryonic electron cell arrays, when g decreases from 0.9 to 0.5, the proportion of auxiliary resources (α) within electron cells hardly increases. When g decreases from 0.5 to 0.1, the proportion of auxiliary resources within electron cells begins to increase, but the increase is very small. For the number of working cell columns (n) in the array being 20, 30, 40, 50, 60, and 70, when the cell particle size (g) is 0.1, the increases in the proportion of auxiliary resources within electron cells are 0.0211, 0.0155, 0.0122, 0.0100, 0.0085, and 0.0074, respectively, which are negligible relative to the electron cell resources. Therefore, in row removal self-repairing arrays, designers can design electron cell particle sizes as small as possible based on their design capabilities.

[0040] 2. Optimal particle size of electron cells within the cell removal self-repair array To verify the effectiveness of the method for optimizing the granularity of electron cells within the embryonic electron cell array, the artificial immune algorithm and the proposed improved adaptive artificial immune algorithm were simultaneously used to independently optimize the selection of electron cell granularity in the application design process of the six scale functional circuits in Table 1 30 times. Taking one optimization process of electron cell granularity optimization in the circuit application design process as an example, the optimization process of electron cell granularity in the application design process of the six scale functional circuits is as follows: Figure 3 As shown in Table 2, after 30 independent optimizations using the immune algorithm, the optimal electron cell granularity for the six functional circuits in the application design process is as follows. Table 3 shows the analysis of the results of 30 independent optimization experiments for the electron cell granularity optimization of the six functional circuits in the application design process using the two algorithms.

[0041] Figure 3 This section describes the first optimization process of the electronic cell granularity optimization problem in the design of six functional circuits, under two artificial immune algorithms. "AIA" represents the artificial immune algorithm, and "IAAIA" represents the improved adaptive artificial immune algorithm. Figure 3 (a) to Figure 3 m in (f) n is 20 20 to 70 In the optimization process of the six functional circuits at the electron cell granularity, the MTTF of the six functional circuits continuously increases with the increase of the algorithm iteration number G, and then increases to a maximum value and remains unchanged. During the electron cell granularity optimization process in the application design of the six functional circuits, the number of iterations G required by IAAIA is less than 20, while the number of iterations G required by AIA is greater than 100. Therefore, IAAIA can effectively improve the convergence speed of the algorithm.

[0042] Table 2. Optimization Results of Electron Cell Granularity in the Design Process of Six Functional Circuits Table 2 Cell granularity optimization results of 6 kinds of functional circuits in design process

[0043] In Table 2, after 30 independent optimizations using two artificial immune algorithms, the optimal electron cell granularity (g) for the six functional circuits were 0.55, 0.5921, 0.670, 0.8267, 0.8789, and 1.0310, respectively, corresponding to auxiliary resource ratios (α) within the electron cells of 0.5146, 0.4433, 0.4076, 0.3439, 0.3238, and 0.2886. Using the optimized electron cell granularity, the MTTF increase for the six functional circuits was 1.203. 104 h, 1.5712 104 h, 8.038 103 h, 4.249 103 h, 2.292 103 h and 2.164 Over 103 hours, the corresponding MTTF growth rates for the circuits were 10.7%, 20.35%, 15.04%, 11.89%, 10.51%, and 20.88%, respectively. In conclusion, optimizing the granularity of the functional circuit application design process can effectively improve the circuit's MTTF, with MTTF increases exceeding 10% in all cases.

[0044] Table 3. Analysis of the results of 30 rounds of electron cell particle size optimization in the design process of 6 functional circuits. Table 3 Analysis results of 30 times cell granularity optimization for 6 kinds of functional circuits

[0045] As shown in Table 3, for the six functional circuits, the two artificial immune algorithms underwent 30 independent optimizations. The average number of iterations for IAAIA were 11.1667, 10.8333, 10.9333, 10.5, 12.1, and 11.4333, respectively, while the average number of iterations for AIA were 156.9, 151.7, 145.3333, 153.1333, 150.0333, and 148.3, respectively. Therefore, IAAIA can effectively improve the convergence speed of the algorithm compared to AIA. The variances of the number of iterations using IAAIA for the six functional circuits were 18.3893, 19.2916, 19.6943, 16.171, 17.6267, and 19.4773, respectively. The variances of the number of iterations using AIA were 101.4036, 85.3833, 98.7049, 73.807, 95.3885, and 95.2591, respectively. Therefore, IAAIA exhibits better stability than AIA. For the six functional circuits, IAAIA achieved a 100% success rate in finding the optimal solution after 30 iterations, while AIA achieved success rates of 96.67%, 93.33%, 90%, 93.33%, 90%, and 96.67%, respectively. Therefore, IAAIA effectively improves the convergence accuracy of the algorithm.

[0046] Device Example 1 According to embodiments of the present invention, an apparatus for optimizing the particle size of an embryonic electronic cell array is provided. Figure 4 This is a schematic diagram of the embryonic electronic cell array electronic cell particle size optimization device according to an embodiment of the present invention, as shown below. Figure 4 As shown, the embryonic electronic cell array electronic cell particle size optimization device according to an embodiment of the present invention specifically includes: The first establishment module 40 is used to acquire electron cell particle size parameters and establish an embryonic electron cell array reliability analysis model and an intracellular auxiliary resource ratio analysis model based on the electron cell particle size parameters; the first establishment module 40 is specifically used for: Obtain two electronic cell particle size parameters: cell particle size g and the proportion of intracellular accessory resources α. Determine the area S of the cell unit g Given gS0 / (1 - α), the number of rows m of the working electron cell array. g = , number of columns n g = The number of rows M of the embryonic electronic cell array g = Number of columns N g = Calculate the failure rate λ of electron cells g= gλ / (1 - α), where, This indicates a round-down operation; the working electron cell array in the embryonic electron cell array is m. n, m, and n' represent the number of cell rows and columns in the working array, respectively, and the size of the embryonic electronic cell array is M. N, M and N are the number of rows and columns of cells in the array, respectively; The reliability R of the embryonic electronic cell array is calculated using Formulas 1 and 2. g (t): Formula 1; Formula 2; Where p(t) is the probability that an electron cell row can function normally; The mean time to failure (MTTF) of the embryonic electron cell array is calculated according to Formula 3. g : Formula 3; According to Formula 4, the proportion of auxiliary resources α within the electron cells after the change in electron cell particle size in the embryonic electron cell array is calculated as follows: Formula 4; Where α0 is the proportion of intracellular accessory resources when cell size g = 1, and H c This represents the hardware resource consumption of a single electron cell within an embryonic electron cell array at a baseline electron cell granularity. , This refers to the hardware resource consumption of a single electron cell within the embryonic electron cell array after changes in cell granularity. .

[0047] The second module 42 is used to establish an electronic cell particle size optimization model based on the embryonic electronic cell array reliability analysis model and the intracellular auxiliary resource ratio analysis model, with the goal of maximizing the reliability of the embryonic electronic cell array. The second establishment module 42 is specifically used for: The optimal model for electron cell size within the array is determined based on formulas 5 and 6: Formula 5; Formula 6; Where F(g, α) is the fitness function of Formula 5; is the penalty term, and μ is the penalty coefficient.

[0048] The calculation module 44 is used to determine the adaptive immune operator based on the number of algorithm iterations and the population quality, thereby determining the improved adaptive artificial immune algorithm. Based on the electronic cell granularity optimization model, the improved adaptive artificial immune algorithm is used to solve the problem of optimizing the electronic cell granularity of the embryonic electronic cell array.

[0049] The calculation module 44 is specifically used for: The adaptive excitation degree calculation operator is determined according to Formula 7: Formula 7; Where NP is the size of the antibody population, f aff (A i ) is antibody A i affinity, f den (A i ) is antibody A i concentration, f sim (A i ) is antibody A i The motivation level, β is the affinity weighting coefficient, and G is the current iteration number of the algorithm. max The maximum number of iterations in the algorithm; The adaptive immune selection operator is determined according to Formula 8: Formula 8; Comparing the activation levels of the Gth generation antibodies, f sim (A i )and favg Based on the relationship between sim(G), the antibody set entering the clonal selection operation in the Gth generation is: Formula 9; Among them, f avg sim(G) is the average excitation level of the Gth generation antibody, and M(G) is the set of antibodies selected for clonal selection operation in the Gth generation antibody; Calculate the adaptive cloning operator according to formula 10: Formula 10; Among them, C(A) i ) is antibody A i The number of clones, round(.) is the floor function, w c Here, CL represents the cloning coefficient, and CL represents the population size of the antibody to be cloned. C is a constant greater than 1; Calculate the adaptive mutation operator according to Formula 11: Formula 11; in, These are mutated antibodies. γ i Let N(0,1) be a random variable in the range [-1,1], and let G(0,1) be a Gaussian variable following a standard normal distribution. Let G be the current iteration algebra. max For the maximum iterative algebra, A max A is the antibody with the highest motivation value in the population. min Let be the antibody with the smallest activation value in the population, and rand be a random variable in the range [0,1]. Calculate the adaptive clonal suppression operator according to formula 12-13: Formula 12; Formula 13; in, These are antibodies that have undergone cloning and mutation. Here, Ai(G+1) represents the antibody after clonal inhibition, and U(0,1) represents a random real number between 0 and 1. j The rules for determining the value of f are as follows: If f aff (B i (G)) ≤ f aff (A i (G)), and A i If (G) is the optimal antibody in the current population, then p j = 0; if f aff (B i (G))>f aff (A i (G)), then p j = 1; if f aff (B i (G)) ≤ f aff (A i (G)), and A i If (G) is not the optimal antibody in the current population, then p j Determined according to Formula 14: Formula 14; Among them, γ>0 is a value related to population diversity. Generally, the better the diversity, the larger the value of γ, and vice versa.

[0050] The embodiments of the present invention are system embodiments corresponding to the above method embodiments. The specific operation of each module can be understood by referring to the description of the method embodiments, and will not be repeated here.

[0051] Device Example 2 This invention provides an embryonic electronic cell array electronic cell particle size optimization device, such as... Figure 5As shown, it includes: a memory 50, a processor 52, and a computer program stored in the memory 50 and executable on the processor 52, wherein the computer program, when executed by the processor 52, performs the steps as described in the method embodiment.

[0052] Device Example 3 This invention provides a computer-readable storage medium storing an information transmission implementation program, which, when executed by a processor 52, performs the steps described in the method embodiment.

[0053] The computer-readable storage media described in this embodiment include, but are not limited to, ROM, RAM, disk, or optical disk.

[0054] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for optimizing the particle size of an embryonic electronic cell array, characterized in that, include: Obtain the electron cell particle size parameters, and establish an embryonic electron cell array reliability analysis model and an intracellular auxiliary resource ratio analysis model based on the electron cell particle size parameters; Based on the aforementioned embryonic electronic cell array reliability analysis model and intracellular auxiliary resource ratio analysis model, an electronic cell granularity optimization model is established with the goal of maximizing the reliability of the embryonic electronic cell array. The adaptive immune operator is determined based on the number of algorithm iterations and population quality, thereby determining the improved adaptive artificial immune algorithm. Based on the described electronic cell granularity optimization model, the improved adaptive artificial immune algorithm is used to solve for the optimal electronic cell granularity of the embryonic electronic cell array. Specifically, determining the adaptive immune operator based on the number of algorithm iterations and population quality includes: The adaptive excitation degree calculation operator is determined according to Formula 7: Official 7; Where NP is the size of the antibody population, f aff (A i ) is antibody A i affinity, f den (A i ) is antibody A i concentration, f sim (A i ) is antibody A i The motivation level, β is the affinity weighting coefficient, and G is the current iteration number of the algorithm. max The maximum number of iterations in the algorithm; The adaptive immune selection operator is determined according to Formula 8: Official 8; Comparing the activation levels of the G-generation antibodies, f sim (A i )and favg Based on the relationship between sim(G), the antibody set entering the clonal selection operation in the Gth generation is: Official 9; Among them, f avg sim(G) is the average excitation level of the Gth generation antibody, and M(G) is the set of antibodies selected for clonal selection operation in the Gth generation antibody; Calculate the adaptive cloning operator according to formula 10: Official 10; Among them, C(A) i ) is antibody A i The number of clones, round(.) is the floor function, w c Here, CL represents the cloning coefficient, and CL represents the population size of the antibody to be cloned. C is a constant greater than 1; Calculate the adaptive mutation operator according to Formula 11: Official 11; in, These are mutated antibodies. γ i Let N(0,1) be a random variable in the range [-1,1], and let G(0,1) be a Gaussian variable following a standard normal distribution. Let G be the current iteration algebra. max For the maximum iterative algebra, A max A is the antibody with the highest motivation value in the population. min Let be the antibody with the smallest activation value in the population, and rand be a random variable in the range [0,1]. Calculate the adaptive clonal suppression operator according to formula 12-13: Official 12; Official 13; in, These are antibodies that have undergone cloning and mutation. Here, Ai(G+1) represents the antibody after clonal inhibition, and U(0,1) represents a random real number between 0 and 1. j The rules for determining the value of f are as follows: If f aff (B i (G)) ≤ f aff (A i (G)), and A i If (G) is the optimal antibody in the current population, then p j = 0; if f aff (B i (G))>f aff (A i (G)), then p j = 1; if f aff (B i (G)) ≤ f aff (A i (G)), and A i If (G) is not the optimal antibody in the current population, then p j Determined according to Formula 14: Official 14; Here, γ>0 is a value related to population diversity. Generally, the better the diversity, the larger the value of γ, and vice versa.

2. The method according to claim 1, characterized in that, Obtaining electron cell particle size parameters and establishing an embryonic electron cell array reliability analysis model and an intracellular auxiliary resource ratio analysis model based on these parameters specifically includes: Obtain two electronic cell particle size parameters: cell particle size g and the proportion of intracellular accessory resources α. Determine the area S of the cell unit g Given gS0 / (1 - α), the number of rows m of the working electron cell array. g = , number of columns n g = The number of rows M of the embryonic electronic cell array g = Number of columns N g = Calculate the failure rate λ of electron cells g = gλ / (1 - α), where, This indicates a round-down operation; the working electron cell array in the embryonic electron cell array is m. n, m, and n' represent the number of cell rows and columns in the working array, respectively, and the size of the embryonic electronic cell array is M. N, M and N are the number of rows and columns of cells in the array, respectively; The reliability R of the embryonic electronic cell array is calculated using Formulas 1 and 2. g (t): Official 1; Official 2; Where p(t) is the probability that an electron cell row can function normally; The mean time to failure (MTTF) of the embryonic electron cell array is calculated according to Formula 3. g : Official 3; According to Formula 4, the proportion of auxiliary resources α within the electron cells after the change in electron cell particle size in the embryonic electron cell array is calculated as follows: Official 4; Where α0 is the proportion of intracellular accessory resources when cell size g = 1, and H c This represents the hardware resource consumption of a single electron cell within an embryonic electron cell array at a baseline electron cell granularity. , This refers to the hardware resource consumption of a single electron cell within the embryonic electron cell array after changes in cell granularity. .

3. The method according to claim 2, characterized in that, Based on the aforementioned embryonic electronic cell array reliability analysis model and intracellular auxiliary resource ratio analysis model, and with the goal of maximizing the reliability of the embryonic electronic cell array, an electronic cell granularity optimization model is established, specifically including: The optimal model for electron cell size within the array is determined based on formulas 5 and 6: Official 5; Official 6; Where F(g, α) is the fitness function of Formula 5; is the penalty term, and μ is the penalty coefficient.

4. A device for optimizing the particle size of an embryonic electronic cell array, characterized in that, include: The first module is used to acquire electron cell particle size parameters and establish an embryonic electron cell array reliability analysis model and an intracellular auxiliary resource ratio analysis model based on the electron cell particle size parameters. The second module is used to establish an electronic cell granularity optimization model based on the embryonic electronic cell array reliability analysis model and the intracellular auxiliary resource ratio analysis model, with the goal of maximizing the reliability of the embryonic electronic cell array. The computation module is used to determine the adaptive immune operator based on the algorithm iteration count and population quality, thereby determining the improved adaptive artificial immune algorithm. Based on the electron cell granularity optimization model, the improved adaptive artificial immune algorithm is used to solve for the optimal electron cell granularity of the embryonic electron cell array. Specifically, the computation module is used for: The adaptive excitation degree calculation operator is determined according to Formula 7: Official 7; Where NP is the size of the antibody population, f aff (A i ) is antibody A i affinity, f den (A i ) is antibody A i concentration, f sim (A i ) is antibody A i The motivation level, β is the affinity weighting coefficient, and G is the current iteration number of the algorithm. max The maximum number of iterations in the algorithm; The adaptive immune selection operator is determined according to Formula 8: Official 8; Comparing the activation levels of the G-generation antibodies, f sim (A i )and favg Based on the relationship between sim(G), the antibody set entering the clonal selection operation in the Gth generation is: Official 9; Among them, f avg sim(G) is the average excitation level of the Gth generation antibody, and M(G) is the set of antibodies selected for clonal selection operation in the Gth generation antibody; Calculate the adaptive cloning operator according to formula 10: Official 10; Among them, C(A) i ) is antibody A i The number of clones, round(.) is the floor function, w c Here, CL represents the cloning coefficient, and CL represents the population size of the antibody to be cloned. C is a constant greater than 1; Calculate the adaptive mutation operator according to Formula 11: Official 11; in, These are mutated antibodies. γ i Let N(0,1) be a random variable in the range [-1,1], and let G(0,1) be a Gaussian variable following a standard normal distribution. Let G be the current iteration algebra. max For the maximum iterative algebra, A max A is the antibody with the highest motivation value in the population. min Let be the antibody with the smallest activation value in the population, and rand be a random variable in the range [0,1]. Calculate the adaptive clonal suppression operator according to formula 12-13: Official 12; Official 13; in, These are antibodies that have undergone cloning and mutation. Here, Ai(G+1) represents the antibody after clonal inhibition, and U(0,1) represents a random real number between 0 and 1. j The rules for determining the value of f are as follows: If f aff (B i (G)) ≤ f aff (A i (G)), and A i If (G) is the optimal antibody in the current population, then p j = 0; if f aff (B i (G))>f aff (A i (G)), then p j = 1; if f aff (B i (G)) ≤ f aff (A i (G)), and A i If (G) is not the optimal antibody in the current population, then p j Determined according to Formula 14: Official 14; Here, γ>0 is a value related to population diversity. Generally, the better the diversity, the larger the value of γ, and vice versa.

5. The apparatus according to claim 4, characterized in that, The first establishment module is specifically used for: Obtain two electronic cell particle size parameters: cell particle size g and the proportion of intracellular accessory resources α. Determine the area S of the cell unit g Given gS0 / (1 - α), the number of rows m of the working electron cell array. g = , number of columns n g = The number of rows M of the embryonic electronic cell array g = Number of columns N g = Calculate the failure rate λ of electron cells g = gλ / (1 - α), where, This indicates a round-down operation; the working electron cell array in the embryonic electron cell array is m. n, m, and n' represent the number of cell rows and columns in the working array, respectively, and the size of the embryonic electronic cell array is M. N, M and N are the number of rows and columns of cells in the array, respectively; The reliability R of the embryonic electronic cell array is calculated using Formulas 1 and 2. g (t): Official 1; Official 2; Where p(t) is the probability that an electron cell row can function normally; The mean time to failure (MTTF) of the embryonic electron cell array is calculated according to Formula 3. g : Official 3; According to Formula 4, the proportion of auxiliary resources α within the electron cells after the change in electron cell particle size in the embryonic electron cell array is calculated as follows: Official 4; Where α0 is the proportion of intracellular accessory resources when cell size g = 1, and H c This represents the hardware resource consumption of a single electron cell within an embryonic electron cell array at a baseline electron cell granularity. , This refers to the hardware resource consumption of a single electron cell within the embryonic electron cell array after changes in cell granularity. .

6. The apparatus according to claim 5, characterized in that, The second establishment module is specifically used for: The optimal model for electron cell size within the array is determined based on formulas 5 and 6: Official 5; Official 6; Where F(g, α) is the fitness function of Formula 5; is the penalty term, and μ is the penalty coefficient.

7. A device for optimizing the particle size of an embryonic electronic cell array, characterized in that, include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program, when executed by the processor, implements the steps of the method for preferred electron cell size of an embryonic electron cell array as described in any one of claims 1 to 3.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores an information transmission implementation program, which, when executed by a processor, implements the steps of the method for optimizing the electronic cell particle size of an embryonic electronic cell array as described in any one of claims 1 to 3.

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