Large-scale design-oriented high-efficiency area and line length optimization macro layout method

Abstract

The invention provides a large-scale design-oriented high-efficiency area and line length optimization macro layout method. The method comprises the following steps of: importing a macro layout stage data set; configuring preset parameters, generating a random initial solution by adopting a sequence pair method, obtaining the line length and the area of the initial solution, and taking the initial solution as a starting point of an optimal solution; performing disturbance to generate a new candidate solution, and obtaining a new candidate line length and a new candidate area of the new candidate solution; obtaining a weighted ratio of the new candidate solution to the initial solution through an objective function; determining whether the new candidate solution meets an acceptance condition or not according to the weighted ratio and a preset acceptance criterion; and continuously judging whether the new candidate solution is superior to the optimal solution or not according to the updating strategy. According to the method, the convergence speed can be increased, the optimal solution can be more effectively explored in a complex design space, the two targets of area minimization and line length shortening can be balanced, and the method adapts to the situation of large-scale design in an actual industrial environment.

Description

Technical Field

[0001] The present invention relates to the technical field of digital integrated circuit electronic design automation (EDA), and in particular to a high-efficiency area and line length optimized macro layout method for large-scale design. Background Art

[0002] The physical design of very large scale integrated circuits (VLSI) covers several key steps, including netlist partitioning, chip floorplanning, placement, clock tree synthesis, routing and timing closure. As an indispensable part of the integrated circuit design process, physical design has a profound impact on chip performance, which determines important indicators such as area, gain, delay and reliability. Therefore, considering physical design elements early in the design stage can more effectively optimize the overall architecture and performance.

[0003] As a key starting stage of IC physical design, floorplanning aims to assign the most suitable position and direction to each module to optimize key indicators including chip area and wire length. This process is also called macro cell layout. The quality of macro layout not only directly affects the optimization effect of subsequent layout and routing steps, but also has a profound impact on the overall performance of the final chip. Due to the mutual constraints between these optimization goals, the macro layout problem is essentially an NP-Hard problem, which means that finding the global optimal solution may require extremely high computing resources.

[0004] However, although there are many improvements in the existing technology to reduce resource requirements and improve efficiency, these improvements are often focused on a single, specific aspect and have limited effect on a wide range of industrial applications. Especially when dealing with large-scale designs, existing methods have the problem of insufficient time efficiency and are difficult to apply to the complex requirements in actual industrial environments.

[0005] In view of this, an area and line length layout method for large-scale design that can adapt to the actual industrial environment needs to be studied urgently. Summary of the invention

[0006] The object of the present invention is to provide an efficient area and line length optimized macro layout method for large-scale design, so as to solve at least one technical problem in the prior art.

[0007] The technical solution of the present invention is:

[0008] An efficient area and wirelength optimized macro placement method for large-scale designs, comprising:

[0009] Importing the MCNC data set and the GSRC data set, and analyzing the modules and netlist information in the MCNC data set and the GSRC data set;

[0010] Configure preset parameters, generate a random initial solution using a sequence pair method, calculate the initial line length and initial area of ​​the initial solution, and save the initial solution as both the current solution and the starting point of the best solution;

[0011] Perturbing to generate the new candidate solution, and obtaining a new candidate line length and a new candidate area of ​​the new candidate solution; obtaining a weighted ratio Delta of the new candidate solution to the initial solution through an objective function;

[0012] Determining whether the new candidate solution meets a preset acceptance criterion according to the weighted ratio Delta and the acceptance criterion;

[0013] When the new candidate solution is accepted as the current solution, whether the new candidate solution is better than the optimal solution is continuously determined according to the update strategy.

[0014] The determining whether the new candidate solution satisfies a preset acceptance criterion according to the weighted ratio Delta and the acceptance criterion includes:

[0015] If Delta is less than 1 or the new candidate solution satisfies the preset acceptance criteria, the new candidate solution satisfies the preset acceptance criteria, and the new candidate solution is accepted as the current solution, and the number of acceptances is increased by one;

[0016] Otherwise, the number of rejections increases by one, and the adaptive temperature adjustment mechanism is triggered when the number of rejections reaches the rejection threshold.

[0017] The adaptive temperature adjustment mechanism comprises:

[0018] High temperature stage: No temperature adjustment is performed:

[0019] T new =T;

[0020] Moderate temperature stage: Multiply the temperature by a temperature adjustment factor to avoid falling into an infinite loop:

[0021] T new =T×temperature_increase_factor;

[0022] Low temperature stage: If none of the above conditions are met, it means that the current temperature is already very low and most attempts are rejected. At this time, set early_stop = True, indicating that the algorithm should be terminated early:

[0023]

[0024] Among them, T newis the new temperature after the adaptive temperature adjustment mechanism; T is the temperature; temperature_increase_factor is the temperature adjustment factor; T mid The current temperature is in the medium temperature stage; r is the number of rejections; rejection_threshold is the rejection threshold.

[0025] The step of continuing to judge whether the new candidate solution is better than the optimal solution according to the update strategy includes:

[0026] If the new candidate solution is superior to the best solution, the best solution and the current solution are updated, and the temperature and the number of local searches are changed;

[0027] If not, the new candidate solution is updated as the current solution, while changing the temperature and the number of local searches.

[0028] The objective function, based on the weighted ratio between the new candidate solution and the current solution, is as follows:

[0029]

[0030] Among them, a is the weight factor; new_wl and cur_wl are the line length of the new candidate solution and the line length of the current solution respectively; new_area and cur_area are the area of ​​the new candidate solution and the area of ​​the current solution respectively.

[0031] The preset acceptance criteria include:

[0032]

[0033] Among them, Delta is the weighted ratio of the new candidate solution to the initial solution; k is a configuration parameter.

[0034] The step of continuing to judge whether the new candidate solution is better than the optimal solution according to the update strategy includes:

[0035] if((new_area<area_tolerance×best_area)and(new_wl<wl_tolerance×best_wl));

[0036] Among them, new_area is the area of ​​the new candidate solution; new_wl is the line length of the new candidate solution;

[0037] area_tolerance and wl_tolerance are area tolerance and line length tolerance respectively; best_area is the best area; best_wl is the best line length.

[0038] The beneficial effects of the present invention include at least:

[0039] The method described in the present invention optimizes the classic simulated annealing algorithm in many aspects, not just in one aspect. Specifically, it optimizes multiple key links of the algorithm at the same time, such as acceptance criteria and termination conditions. Optimizing the simulated annealing algorithm with multiple links has multiple advantages. By introducing a new cost difference formula and a probability-based selection mechanism, the decision-making accuracy and the ability to jump out of the local optimum are improved; the strategy of dynamically updating the best solution ensures that the latest high-quality solution can be recorded even in the case of minor improvements; the intelligent early stopping mechanism is flexibly adjusted according to the current temperature conditions, which improves the efficiency of the algorithm and saves computing resources. This comprehensive optimization strategy can promote the synergy between different improvement links, enhance the global search capability, is suitable for large-scale design challenges, and can effectively explore complex solution spaces. In addition, the method described in the present invention can also accelerate the convergence speed and improve the flexibility and adaptability of the algorithm to deal with different problem scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 A framework diagram of the method of the present invention;

[0041] Figure 2 The result of SAWAL optimizing both area and wire length in test circuit ami49.

[0042] Figure 3 The result diagram of SAWAL optimizing both area and wire length in test circuit n100.

[0043] Figure 4 The result diagram of SAWAL optimizing both area and wire length in test circuit n200.

[0044] Figure 5 The result diagram of SAWAL optimizing both area and wire length in test circuit n300. DETAILED DESCRIPTION

[0045] The present application is further described below in conjunction with the accompanying drawings.

[0046] In the research process of macro layout problem, technicians in this field have proposed various algorithms. According to the principles and application methods of these methods, they can be roughly classified into the following categories: heuristic methods, analytical methods and machine learning methods.

[0047] Heuristic methods can flexibly deal with various complex situations, including NP-Hard problems, and can explore a wider range of potential solution spaces to find better solutions, but they cannot ensure that the global optimal solution is found and are prone to fall into the local optimal solution.

[0048] Analytical methods can directly solve problems through mathematical modeling and obtain theoretically optimal or near-optimal solutions. However, as the scale of the problem increases, the difficulty of solving the problem and the amount of calculation increase exponentially. The actual situation is often more complicated than the model assumptions, which leads to a reduction in effectiveness in practical applications.

[0049] Machine learning methods can automatically learn patterns from data, reduce human intervention, and adjust their behavior based on historical data to adapt to new environments. However, machine learning models are usually trained for specific application scenarios, so their performance may drop significantly when faced with new environments or unseen data types. In addition, machine learning methods usually require a large amount of high-quality data for effective training. However, in the integrated circuit industry, obtaining such data often faces many challenges. Therefore, various methods have advantages and disadvantages, and it is particularly important to try to address the shortcomings of these methods. A lot of research is being conducted on various types of methods.

[0050] For example, an improved simulated annealing method uses two cooling strategies to update the temperature. In the early optimization stage, the temperature is slowly reduced to enhance the global search, and in the later stage, the traditional formula is used to quickly reduce the temperature to focus on the local search. However, since it only optimizes a single point, the overall efficiency improvement is limited.

[0051] For example, a dual-path search method was introduced, which increases the probability of finding a better local optimal solution. Compared with a single-path search, this method can better explore areas far away from the current solution space, thereby improving the quality of the final solution. However, since its experimental process only focuses on the optimization of a single objective, area, this method obviously has limitations for macro layout problems involving multiple complex objectives.

[0052] For example, the new lion group heuristic algorithm is used to solve the multi-objective optimization problem in VLSI layout planning, but the multi-objective optimization effect is only reflected in small-scale data and cannot achieve line length and area coordinated optimization in large-scale data.

[0053] For example, the method of introducing parallel computing mechanism effectively solves the problem that the classic particle swarm optimization (PSO) algorithm is prone to fall into local optimality. However, this method, like the method proposed by Vatansever M, also only focuses on the single objective optimization of area. For macro layout problems involving multiple complex objectives, this single objective optimization method has obvious limitations.

[0054] For example, DPAHMA redefines these genetic operations for the complex crossover operations and mutation operations that limit diversity in AHMA, which helps explore a wider solution space. However, this method takes a long time to calculate for very large-scale or more complex circuit designs, and the effect of joint optimization of line length and area is not obvious.

[0055] It can be seen from the existing methods that improvements in existing technologies are often focused on a single, specific aspect. For example, some studies may only optimize a certain performance indicator, while others may focus on the improvement of small-scale designs. However, although such improvement measures are effective in specific situations, they are limited for a wide range of industrial applications. Especially when dealing with large-scale designs, these methods may have problems with insufficient time efficiency and are difficult to apply to the complex requirements in actual industrial environments.

[0056] Therefore, the core purpose of the area and line length layout method for large-scale design provided in this embodiment is to provide a more efficient, flexible and more global search capable optimization method through the simultaneous optimization of multiple key links of the simulated annealing algorithm to better serve the needs of actual industrial applications.

[0057] In order to explain the present invention in detail, the following embodiments are provided: Specific embodiment 1:

[0059] The present invention provides an embodiment:

[0060] The method proposed in this embodiment is named SAWAL (Simulated Annealing with Advanced Wire and Area Optimization for Large-scale Macro Placement), and its overall system flow is as follows: Figure 1 Please note that the methods described below in this article are all replaced by SAWAL.

[0061] The process starts with importing and parsing the MCNC and GSRC datasets. Subsequently, the SAWAL method enters the initialization phase, which includes configuring the necessary parameters and using the sequence pair method to generate a random initial solution, calculating the line length and area of ​​the solution, and this initial solution will be saved as both the current solution and the starting point of the best solution. Next, the algorithm generates a new candidate solution through a perturbation mechanism, and evaluates the quality of this new solution according to the preset acceptance criteria. If it is not accepted, the temperature is adjusted and the perturbation continues to generate a new solution. If the new solution is accepted, it is further judged whether this solution is better than the existing best solution. If it is better than the best solution, the current solution and the best solution will be updated at the same time, otherwise it will only be updated to the current solution. The specific details and innovations will be described in detail below.

[0062] Calculation of the objective function

[0063] In macro placement optimization, objective functions are usually used to guide the optimization of module positions and their aspect ratios to achieve satisfactory placement results. Traditionally, these objective functions include total line length, area, or a weighted sum of the two.

[0064] Cost = area (1)

[0065] Cost = wl (2)

[0066] Cost = α × area + β × wl (3)

[0067] This paper proposes a new objective function, which is based on the weighted ratio between the new solution and the previously saved current solution. The specific formula is as follows:

[0068]

[0069] Among them, a is the weight factor used to balance the impact of line length and area on the overall cost; new_wl and cur_wl are the line lengths of the new and old solutions respectively; new_area and cur_area are the areas of the new and old solutions respectively.

[0070] This objective function is designed to comprehensively consider the two key factors of line length and area, ensuring that the optimization process will not be overly biased towards one aspect and ignore the importance of the other aspect. The traditional weighted sum relies on absolute values. When the data ranges of different dimensions vary greatly, one dimension may dominate the optimization process and weaken the influence of other dimensions. At the same time, the existing objective function is as follows:

[0071]

[0072] In contrast, the objective function proposed in this paper provides a more dynamic and locally sensitive method to evaluate the improvement of the new solution relative to the current solution. It helps to better capture the small changes in each iteration, maintain the flexibility of the algorithm, and simplify the calculation.

[0073] Preset acceptance criteria and solution update strategy

[0074] Since the objective function is different from the traditional method, its preset acceptance criteria are also adjusted. When evaluating the newly generated candidate solutions, SAWAL uses the acceptance probability improved based on the Metropolis criterion. The traditional preset acceptance criteria are as follows:

[0075] Delta = Cost_new - Cost_old (6)

[0076]

[0077] The default acceptance criteria proposed in this paper are as follows:

[0078]

[0079] Here, if the new solution is better, that is, Delta < 1, or passes certain probability conditions (depending on the temperature and cost differences, as well as the parameter k we passed in), then the new solution is accepted. This allows the algorithm to explore a larger solution space in the early stages and gradually shrink to the vicinity of the local optimal solution.

[0080] Once a new solution is accepted, it is next checked whether it is better than the existing best solution:

[0081] if((new_area<area_tolerance×best_area)and(new_wl<wl_tolerance×best_wl)) (9)

[0083] Among them, area_tolerance and wl_tolerance are the tolerances for area and line length, respectively, and their sizes are slightly greater than 1. The best solution will be updated only when the area and line length of the new solution are slightly better or almost the same as the currently recorded best solution. The tolerance is slightly greater than 1 to avoid stopping the search too early due to too strict conditions. Traditional methods usually judge whether to accept a new solution by comparing the new and old objective functions. This method makes it difficult to ensure that both the area and line length are optimized at the same time.

[0084] In contrast, the method proposed in this paper effectively solves this problem by introducing a tolerance mechanism, ensuring that the two objectives (area and line length) can be optimized simultaneously, avoiding the trap of single-objective optimization.

[0085] Among them, the adaptive temperature adjustment mechanism includes:

[0086] In order to improve search efficiency and avoid wasting computing resources in low temperature conditions, SAWAL introduces an adaptive temperature adjustment mechanism, which dynamically adjusts the temperature according to the acceptance of the solution in the current iteration, ensuring that the algorithm can fully explore the solution space and terminate unnecessary calculations in a timely manner.

[0087] In each iteration, the algorithm counts the proportion of new solutions that are accepted. If a candidate solution above the rejection threshold rejection_threshold (e.g., 90%) is rejected, the temperature adjustment mechanism is triggered, and the operation of the mechanism under various temperature conditions is as follows.

[0088] High temperature stage: No temperature adjustment is performed. This means that the current temperature is still high, and higher temperatures allow more exploration behavior. The algorithm should continue to explore the solution space.

[0089] T new =T (10)

[0090] Moderate temperature stage: The temperature adjustment mechanism is triggered at this temperature stage, which is likely due to falling into a local optimal solution. At this time, the temperature is multiplied by a temperature adjustment factor temperature_increase_factor. This helps to moderate the temperature increase, increase exploration, and avoid falling into a local optimal solution. The number of temperature increases is set in advance, generally a very small integer, to avoid falling into an infinite loop.

[0091] T new =T×temperature_increase_factor (11)

[0092] Low temperature stage: If none of the above conditions are met, it means that the current temperature is already very low and most attempts are rejected. In this case, set early_stop = True, indicating that the algorithm should be terminated early. This can avoid meaningless repeated calculations when approaching the optimal solution.

[0093]

[0094] The process of the area and line length layout method for large-scale design, namely SAWAL, described in this embodiment includes the following steps:

[0095] Step 1: Import MCNC and GSRC or other data sets and analyze their modules, netlists and other information.

[0096] Step 2: Initialization phase, configure the preset parameters of SAWAL, use the sequence pair method to generate a random initial solution, calculate the line length and area of ​​the initial solution, and save this initial solution as both the current solution and the starting point of the best solution.

[0097] Step 3: Determine whether the stopping condition is reached. If the stopping condition is reached, output the best solution. Otherwise, continue to the next step.

[0098] Step 4: Perturb to generate new candidate solutions. Apply SAWAL's perturbation mechanism to generate a new candidate solution and calculate the line length and area of ​​the new solution.

[0099] Step 5: Calculate the objective function, which is the weighted ratio Delta of the new solution and the old solution.

[0100] Step 6: If Delta is less than 1, the new solution is accepted as the current solution, and the number of acceptances (AcceptCount) is increased by one; otherwise, if the new solution meets the acceptance criteria based on Metropolis, the new solution is also accepted as the current solution, and the number of acceptances (AcceptCount) is increased by one, otherwise the number of rejections (RejectCount) is increased by one, triggering the adaptive temperature adjustment mechanism, and then proceed to step 3 to continue execution.

[0101] Step 7: After accepting the new solution, continue to determine whether the new solution is better than the best solution according to the update strategy set by SAWAL. If so, update the best solution and the current solution, and change the temperature and the number of local searches. If not, only update to the current solution, and change the temperature and the number of local searches. Then proceed to step 3.

[0102] Verification stage:

[0103] The area and line length layout method for large-scale design described in this embodiment, namely SAWAL, has been verified on two authoritative open source benchmarks in the industry. In academia, the classic test data used to verify the effect of layout algorithms comes from circuit design examples released by Microelectronic Centre for North Carolina and Gigascale System Research Centre. These data sets are referred to as MCNC test sets and GSRC test sets, respectively. There are differences in the circuit scale of these two data sets, as follows:

[0104] Table 1 Characteristics of MCNC circuit

[0105]

[0106] Table 2 Characteristics of GSRC circuit

[0107]

[0108] The MCNC and GSRC datasets mainly contain three specific formats of files (.blocks, .nets, and .pl). The .blocks file lists in detail the specific types, size specifications, and I / O port configurations of all modules in the design. The .nets file describes the number of pins and nets included in the design, and records in detail the basic information and connections of the elements in each net.

[0109] The hardware environment relied on by all experiments in this embodiment is configured as a platform running the Ubuntu 20.04.6 desktop operating system; the processor core is Intel(R) Core(TM) i5-10600KF CPU@4.10GHz, and programming is done in Python.

[0110] The hyperparameter settings of the area and wirelength layout method for large-scale design, namely SAWAL, described in this embodiment are shown in Table 3:

[0111] Table 3 Hyperparameter settings of SAWAL

[0112]

[0113] This experiment compares the SAWAL method proposed in this embodiment with the DPAHMA proposed in the literature (Jiang L, Ouyang D, Zhou H, et al. DPAHMA: a novel dual-population adaptive hybrid memetic algorithm for non-slicing VLSI floorplans [J]. The Journal of Supercomputing, 2023, 79 (14): 15496-15534.), and the baseline of its paper, the AHMA proposed in the literature (Chen J, Liu Y, Zhu Z, et al. Anadaptive hybrid memetic algorithm for thermal-aware non-slicing VLSI floorplanning [J]. Integration, 2017, 58: 245-252.). The area and line length are optimized at the same time. Since AHMA and DPAHMA do not use n10, n30, and n100 for experiments, their results are not compared. The remaining comparative experimental results are shown in Tables 4 and 5.

[0114] Table 4 Experimental results on MCNC

[0115]

[0116] Table 5 Experimental results on GSRC

[0117]

[0118] From the comparison of the experimental results of the three algorithms on the MCNC and GSRC datasets, the SAWAL algorithm shows significant advantages, especially in large-scale datasets. The following is a detailed analysis of the performance of the algorithm on these two datasets.

[0119] On the MCNC dataset, for the apte and hp test circuits, SAWAL has the best area and wire length. For the ami33 and ami49 test circuits, although AHMA and DPAHMA each achieved a smaller wire length on one test circuit, SAWAL maintained the smallest area advantage on these two circuits. Overall, on the MCNC dataset, SAWAL shortened the wire length by 9.71% and reduced the area by 0.67% compared to DPAHMA. Compared to AHMA, the wire length was shortened by 9.92% and the area was reduced by 0.94%. Figure 2 The visualization of the optimization solution of the SAWAL algorithm on the ami49 test circuit is shown.

[0120] SAWAL performs particularly well on the more complex and larger GSRC dataset. Although AHMA has a slight advantage in area on the n100 test circuit, its line length is much longer than SAWAL. SAWAL shows a clear advantage over the other two algorithms on the n200 and n300 test circuits. Overall, on the GSRC dataset, SAWAL shortens the line length by 24.16% and reduces the area by 2.09% compared to DPAHMA. Compared with AHMA, SAWAL shortens the line length by 25.58% and reduces the area by 1.47%. It is particularly noteworthy that while maintaining an advantage in area, SAWAL's advantage in line length optimization is particularly obvious. Figure 3 , Figure 4 , Figure 5 The visualization of the optimization solution of the SAWAL algorithm on the GSRC dataset is shown.

[0121] In summary, in the field of multi-objective optimization, the SAWAL algorithm shows significant advantages over AHMA and DPAHMA, especially when dealing with large-scale designs, which may be due to the objective function and update strategy adopted by SAWAL. These characteristics of SAWAL enable it to more effectively explore the optimal solution in a complex design space while balancing multiple optimization objectives, such as minimizing area and shortening line length. This excellent multi-objective optimization capability is particularly important for modern complex integrated circuit design.

[0122] It should be clarified that: in the area and line length layout method for large-scale design described in this embodiment, the layout representation method used is a sequence pair, and BSG, O-tree, B*-tree, MP-tree, CP-tree, Polish expression, corner block column and the like can all be used as layout methods, so they can replace the sequence pair of this embodiment; for the acceptance probability improved based on the Metropolis criterion, its purpose is to accept the solution when Delta is less than 1, and gradually approach 0 when it is greater than 1. An alternative is to use the Sigmoid function for calculation. It can also be close to 1 when Delta is less than 1, and quickly approach 0 when it is greater than 1. For example, linear attenuation function, exponential attenuation function, etc., which do not deviate from the core technology of this method and will not be repeated in this article.

[0123] The above disclosures are only several specific implementation scenarios of the present invention, but the present invention is not limited thereto, and any changes that can be thought of by a person skilled in the art should fall within the protection scope of the present invention. The above serial numbers of the present invention are only for description and do not represent the advantages and disadvantages of the implementation scenarios.

Claims

1. An efficient area and line length optimized macro layout method for large-scale design, characterized in that: include: Importing the MCNC data set and the GSRC data set, and analyzing the modules and netlist information in the MCNC data set and the GSRC data set; Configure preset parameters, generate a random initial solution using a sequence pair method, calculate the initial line length and initial area of ​​the initial solution, and save the initial solution as both the current solution and the starting point of the best solution; Perturbing to generate the new candidate solution, and obtaining a new candidate line length and a new candidate area of ​​the new candidate solution; obtaining a weighted ratio Delta of the new candidate solution to the initial solution through an objective function; Determining whether the new candidate solution meets a preset acceptance criterion according to the weighted ratio Delta and the acceptance criterion; When the new candidate solution is accepted as the current solution, whether the new candidate solution is better than the optimal solution is continuously determined according to the update strategy.

2. The efficient area and line length optimized macro layout method for large-scale design according to claim 1, characterized in that: The determining whether the new candidate solution satisfies a preset acceptance criterion according to the weighted ratio Delta and the acceptance criterion includes: If Delta is less than 1 or the new candidate solution satisfies the preset acceptance criteria, the new candidate solution satisfies the preset acceptance criteria, and the new candidate solution is accepted as the current solution, and the number of acceptances is increased by one; Otherwise, the number of rejections increases by one, and the adaptive temperature adjustment mechanism is triggered when the number of rejections reaches the rejection threshold.

3. The efficient area and line length optimized macro layout method for large-scale design according to claim 2, characterized in that: The adaptive temperature adjustment mechanism comprises: High temperature stage: No temperature adjustment is performed: T new =T; Moderate temperature stage: Multiply the temperature by a temperature adjustment factor to avoid an infinite loop: T new =T×temperature_increase_factor; Low temperature stage: If none of the above conditions are met, it means that the current temperature is already very low and most attempts are rejected. In this case, earlystop = True is set, indicating that the algorithm should be terminated early: Among them, T new is the new temperature after the adaptive temperature adjustment mechanism; T is the temperature; temperature_increase_factor is the temperature adjustment factor; T mid The current temperature is in the medium temperature stage; r is the number of rejections; rejection_threshold is the rejection threshold.

4. The efficient area and line length optimized macro layout method for large-scale design according to claim 1, characterized in that: The step of continuing to judge whether the new candidate solution is better than the optimal solution according to the update strategy includes: If the new candidate solution is superior to the best solution, the best solution and the current solution are updated, and the temperature and the number of local searches are changed; If not, the new candidate solution is updated as the current solution, while changing the temperature and the number of local searches.

5. The efficient area and line length optimized macro layout method for large-scale design according to claim 1, characterized in that: The objective function, based on the weighted ratio between the new candidate solution and the current solution, is as follows: Among them, a is the weight factor; new_wl and cur_wl are the line length of the new candidate solution and the line length of the current solution respectively; new_area and cur_area are the area of ​​the new candidate solution and the area of ​​the current solution respectively.

6. The efficient area and line length optimized macro layout method for large-scale design according to claim 1, characterized in that: The preset acceptance criteria include: Among them, Delta is the weighted ratio of the new candidate solution to the initial solution; k is the configuration parameter; T is the temperature.

7. The efficient area and line length optimized macro layout method for large-scale design according to claim 1, characterized in that: The step of continuing to judge whether the new candidate solution is better than the optimal solution according to the update strategy includes: if((new_area<area_tolerance×best_area)and(new_wl <wl_tolerance×best_wl)); Among them, new_area is the area of ​​the new candidate solution; new_wl is the line length of the new candidate solution; area_tolerance and wl_tolerance are the area tolerance and line length tolerance respectively; best_area is the best area; best_wl is the best line length.

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