Parameter configuration method and device for communication link simulation, equipment and medium

Abstract

The invention relates to a parameter configuration method and device for communication link simulation, equipment and a medium. Comprising the steps of screening a corresponding target simulation model from default simulation models based on basic signal features of a to-be-simulated model in a to-be-simulated link, and obtaining a corresponding initial link simulation result based on the target simulation model; determining parameter configuration information corresponding to the link initial simulation result, and determining link simulation parameter configuration information according to the parameter configuration information; extracting a characteristic index of the link simulation parameter configuration information, calculating a margin space corresponding to the characteristic index, and determining a corresponding gradient intensity; and sorting the gradient intensities, screening out target gradient intensities based on a sorting result, and determining the link simulation parameter configuration information corresponding to the target gradient intensities as target parameter configuration. According to the method, the redundant model can be effectively removed, the model configuration is normalized, the simulation tuning efficiency is improved, the evaluation of the configuration parameters is completed by solving the gradient intensity value, and the simulation configuration quality is ensured.

Description

Technical Field

[0001] The present application relates to the field of communication technology, and in particular to a parameter configuration method, device, equipment and medium for communication link simulation. Background Art

[0002] In the communication link system, the configuration of software parameters can optimize the performance of the communication link so that it can transmit data efficiently. For example, by adjusting parameters such as data transmission rate and frame size, it can ensure that the data transmission speed in the communication link reaches the optimal speed, thereby improving the efficiency of the entire communication system.

[0003] There are many methods for optimizing software parameter configuration in communication links, including optimizing the configuration by simulating the communication link. The simulation optimization configuration method has a mature process and controllable simulation results. It can be optimized in advance to save the time window for later debugging, and the debugging of parameter samples is easier to control. The current mainstream simulation models are SPICE model and IBIS model. However, SPICE model has large-scale transistor parameters and it is difficult to quickly solve the results. The IBIS model has a wide variety of models and many redundant models, which are difficult to simply normalize, resulting in a large number of simulation iterations when selecting the optimal configuration, making it difficult to promote and apply on a large scale.

[0004] At present, no effective solution has been proposed for the problems of low simulation efficiency and poor effect in the existing scenarios of simulation optimization of communication links. Summary of the invention

[0005] Based on this, it is necessary to provide a parameter configuration method, device, equipment and medium for communication link simulation to address the above technical problems.

[0006] In a first aspect, the present application provides a method for configuring parameters of a communication link simulation, the method comprising: Based on the preset signal basic characteristics of the to-be-simulated model in the to-be-simulated link, a target simulation model corresponding to the to-be-simulated link is selected from the default simulation model, and the to-be-simulated link is simulated based on the target simulation model to obtain the corresponding initial simulation result of the link; Determine all parameter configuration information of the target simulation model corresponding to the initial simulation result of the link, and determine at least one set of link simulation parameter configuration information according to the parameter configuration information; Extracting characteristic indicators of link simulation parameter configuration information, calculating a margin space corresponding to the characteristic indicator based on data of the characteristic indicator and a preset expected indicator, and determining a gradient strength of the corresponding characteristic indicator according to the margin space; The gradient strengths corresponding to the link simulation parameter configuration information are sorted, the target gradient strength is screened out based on the sorting result, and the link simulation parameter configuration information corresponding to the target gradient strength is determined as the target parameter configuration.

[0007] In one embodiment, based on the preset signal basic characteristics of the model to be simulated in the link to be simulated, selecting a target simulation model corresponding to the link to be simulated from the default simulation model includes: Based on the basic characteristics of the signal, the corresponding initial simulation model is selected from the default simulation model, and the working condition information of each initial simulation model is extracted; According to the preset communication protocol corresponding to the link to be simulated, each initial simulation model is grouped to obtain at least one network group; wherein a network group includes at least two model types; The operating condition information between the initial simulation models corresponding to each model type in the network group is matched pairwise. If it is detected that there is a set of target operating condition information that matches successfully in each model type, the target simulation model corresponding to the target operating condition information is determined according to the matching result.

[0008] In one embodiment, matching the operating condition information between the initial simulation models corresponding to each model type in the network group in pairs includes: Get the preset window constraints; In the time sequence of the operating condition information, dynamic programming calculations are performed pairwise between the operating condition information of different model types belonging to the same network group based on window constraints. The similarity results between the operating condition information of different model types are obtained according to the dynamic programming calculation results. When it is detected that the similarity of the operating condition information of the initial simulation models between each model type is greater than the preset similarity threshold, it is determined that the operating condition information of the initial simulation models corresponding to each model type matches.

[0009] In one embodiment, after selecting the corresponding initial simulation model from the default simulation model based on the basic characteristics of the signal and extracting the working condition information of each initial simulation model, the method further includes: Based on the working condition information, the input and output categories of each initial simulation model are divided to obtain an input category model, an output category model and an input and output category model; For the input category model, the output category model and the input and output category model respectively, the corresponding target simulation model is selected from the default simulation model.

[0010] In one embodiment, determining the gradient strength of the corresponding characteristic index according to the margin space includes: Traverse the feature indicators of each dimension and calculate the gradient value of each dimension respectively; The gradient values ​​corresponding to each dimension are converted into norm form to obtain the gradient strength corresponding to each dimension.

[0011] In one embodiment, sorting the gradient strengths corresponding to the link simulation parameter configuration information and selecting the target gradient strength based on the gradient strengths includes: Each gradient strength is sorted in order from small to large, and the maximum value among the sorted gradient strengths is determined as the target gradient strength.

[0012] In one embodiment, based on the preset signal basic characteristics of the model to be simulated in the link to be simulated, selecting a target simulation model corresponding to the link to be simulated from the default simulation model includes: Determine the basic signal corresponding to the model to be simulated; Defining a target signal set of basic signals, wherein the target signal set includes basic characteristics of signals corresponding to the model to be simulated; The target signal set is mapped and matched with the default signal characteristics corresponding to the default simulation model, and the target simulation model is obtained by screening.

[0013] In a second aspect, the present application also provides a parameter configuration device. The device comprises: An acquisition module is used to select a target simulation model corresponding to the link to be simulated from the default simulation model based on the preset signal basic characteristics of the model to be simulated in the link to be simulated, and simulate the link to be simulated based on the target simulation model to obtain the corresponding initial simulation result of the link; A calculation module is used to determine all parameter configuration information of the target simulation model corresponding to the initial simulation result of the link, and determine at least one set of link simulation parameter configuration information according to the parameter configuration information; extract characteristic indicators of the link simulation parameter configuration information, calculate the margin space corresponding to the characteristic indicator based on the data of the characteristic indicator and the preset expected indicator, and determine the gradient strength of the corresponding characteristic indicator according to the margin space; The generation module is used to sort the gradient strengths corresponding to the link simulation parameter configuration information, screen out the target gradient strength based on the sorting result, and determine the link simulation parameter configuration information corresponding to the target gradient strength as the target parameter configuration.

[0014] In a third aspect, the present application further provides a computer device. The computer device includes a memory and a processor, the memory stores a computer program, and the processor implements the following steps when executing the computer program: Based on the preset signal basic characteristics of the to-be-simulated model in the to-be-simulated link, a target simulation model corresponding to the to-be-simulated link is selected from the default simulation model, and the to-be-simulated link is simulated based on the target simulation model to obtain the corresponding initial simulation result of the link; Determine all parameter configuration information of the target simulation model corresponding to the initial simulation result of the link, and determine at least one set of link simulation parameter configuration information according to the parameter configuration information; Extracting characteristic indicators of link simulation parameter configuration information, calculating a margin space corresponding to the characteristic indicator based on data of the characteristic indicator and a preset expected indicator, and determining a gradient strength of the corresponding characteristic indicator according to the margin space; The gradient strengths corresponding to the link simulation parameter configuration information are sorted, the target gradient strength is screened out based on the sorting result, and the link simulation parameter configuration information corresponding to the target gradient strength is determined as the target parameter configuration.

[0015] In a fourth aspect, the present application further provides a computer-readable storage medium. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the following steps are implemented: Based on the preset signal basic characteristics of the to-be-simulated model in the to-be-simulated link, a target simulation model corresponding to the to-be-simulated link is selected from the default simulation model, and the to-be-simulated link is simulated based on the target simulation model to obtain the corresponding initial simulation result of the link; Determine all parameter configuration information of the target simulation model corresponding to the initial simulation result of the link, and determine at least one set of link simulation parameter configuration information according to the parameter configuration information; Extracting characteristic indicators of link simulation parameter configuration information, calculating a margin space corresponding to the characteristic indicator based on data of the characteristic indicator and a preset expected indicator, and determining a gradient strength of the corresponding characteristic indicator according to the margin space; The gradient strengths corresponding to the link simulation parameter configuration information are sorted, the target gradient strength is screened out based on the sorting result, and the link simulation parameter configuration information corresponding to the target gradient strength is determined as the target parameter configuration.

[0016] The above-mentioned parameter configuration method, device, equipment and medium for communication link simulation first screens out the corresponding target simulation model according to the basic characteristics of the signal of the model to be simulated, and obtains the corresponding initial link simulation result according to the target simulation model; secondly, determines all parameter configuration information corresponding to the initial link simulation result, and determines the link simulation parameter configuration information according to the parameter configuration information; then extracts the characteristic index of the link simulation parameter configuration information, calculates the margin space corresponding to the characteristic index according to the data of the characteristic index and the expected index, determines the corresponding gradient strength according to the margin space, and finally sorts the gradient strength corresponding to the link simulation parameter configuration information, screens out the target gradient strength based on the sorting result, and determines the link simulation parameter configuration information corresponding to the target gradient strength as the target parameter configuration. The present application can effectively remove redundant models, normalize model configurations, improve the efficiency of simulation tuning, and complete the evaluation of configuration parameters by solving the gradient strength value to ensure the quality of simulation configuration. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 A schematic diagram of a flow chart of a parameter configuration method in an embodiment; Figure 2 A schematic flow chart of a parameter configuration method in a preferred embodiment; Figure 3 A schematic diagram of a simulation waveform curve in an embodiment; Figure 4 It is a structural block diagram of a parameter configuration device in one embodiment; Figure 5 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0018] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0019] In one embodiment, Figure 1 As shown, a parameter configuration method for communication link simulation is provided, comprising: Step S110, based on the preset signal basic characteristics of the model to be simulated in the link to be simulated, a target simulation model corresponding to the link to be simulated is selected from the default simulation model, and the link to be simulated is simulated based on the target simulation model to obtain the corresponding link initial simulation result.

[0020] Specifically, the link to be simulated is a communication link that needs to be simulated. A complete link to be simulated usually includes multiple models to be simulated. According to the basic characteristics of the signals of the multiple models to be simulated, the corresponding target simulation model is screened out from the default simulation model. In this embodiment, the default simulation model refers to a preset IBIS model (Input / output buffer information specification); the basic characteristics of the signal refer to the characteristics of several important signals of the model to be simulated, such as signal group, signal transmission type, level voltage, etc., wherein the signal group is generally established for the same type of network such as DDR (Double Data Rate Synchronous Dynamic Random Access Memory), RGMII (Reduced Gigabit Media Independent Interface), etc. The same signal group includes data, address, and control, and different protocols have different classification specifications, that is, in actual applications, the signal group can be determined according to the communication protocol; the above-mentioned signal transmission type is generally one of unidirectional transmission and bidirectional transmission; the above-mentioned level voltage mainly describes the voltage value of the circuit signal in different instantaneous states, and in some embodiments, it is also the maximum tolerance value of the level. Furthermore, the above-mentioned default simulation model is an existing model related to the link to be simulated. In this embodiment, the default simulation model is an existing IBIS model. In summary, in this embodiment, since the basic characteristics of the signal are the key items in the basic parameters of the simulation, and based on the characteristics of the IBIS model simulation, it is necessary to identify the basic characteristics of the signal of the default simulation model and the model to be simulated one by one, and determine the default simulation model with the same or similar basic characteristics of the signal as the target simulation model corresponding to the link to be simulated, so as to ensure the accuracy of the model simulation and the normalization of the model configuration, that is, the model selection needs to meet the requirements of no conflict in the signal transmission type, consistent level voltage and consistent model configuration within the signal group. Finally, the link to be simulated is simulated based on the target simulation model to obtain the initial simulation result of the above-mentioned link.

[0021] Step S120, determining all parameter configuration information of the target simulation model corresponding to the initial link simulation result, and determining at least one set of link simulation parameter configuration information according to the parameter configuration information.

[0022] Specifically, since the signal transmission modes mainly include single drive-single load and single drive-multiple load structures, and the configuration modes between single drives and between single loads and multiple loads are consistent, in order to ensure the adaptability of chip configuration in various practical application projects, the configuration of each device of multiple loads generally adopts the same configuration. Furthermore, in order to verify the simulation effect of the target simulation model, all possible parameter configurations of the target simulation model can be traversed, and the simulation effect of each set of parameter configurations can be calculated, so as to determine a set of parameter configurations with the best simulation effect. In summary, in this embodiment, the screened target simulation models are first arranged and combined to obtain multiple link initial simulation results corresponding to the link to be simulated, and all parameter configuration information that can be obtained by each target simulation model in each link initial simulation result is counted, and the corresponding link simulation parameter configuration information is determined according to each set of parameter configuration information, wherein the above-mentioned link initial simulation result is the arrangement result obtained after the above-mentioned target simulation models are arranged and combined. In practical applications, multiple link initial simulation results are generally obtained according to the target simulation model. Taking the basic CPU-memory bar system as an example, the target simulation model generally includes multiple types of CPUs and multiple types of memory bars. Therefore, after the target simulation models are arranged and combined, multiple link initial simulation results are generally obtained.

[0023] Step S130 , extracting characteristic indicators of link simulation parameter configuration information, calculating a margin space corresponding to the characteristic indicator based on data of the characteristic indicator and a preset expected indicator, and determining a gradient strength of the corresponding characteristic indicator according to the margin space.

[0024] Specifically, characteristic indicators corresponding to the link simulation parameter configuration are first extracted, wherein the characteristic indicators include but are not limited to indicators such as eye diagram, bathtub curve, bit error rate, signal-to-noise ratio, etc. Different signal protocol types have different electrical specification requirements and template indicators. Therefore, in practical applications, the required characteristic indicators can be determined according to actual needs. In some embodiments, the corresponding time domain waveform curve can be calculated based on the link simulation parameter configuration information applied to the target simulation model, and the corresponding characteristic indicators can be extracted based on the time domain waveform curve.

[0025] Furthermore, according to the data of each characteristic index and the corresponding expected index, the corresponding margin space is calculated. The margin space is generally in vector form. In practical applications, there are generally multiple characteristic indexes. At this time, the margin space of each characteristic index needs to be calculated independently. The margin space of each characteristic index parameter and the expected index is calculated as follows: y=[y1(x)-y2(x)] Among them, y represents the parameter of the characteristic index in the y direction, y1(x) represents the data of the characteristic index obtained by simulation, such as the eye diagram, and the characteristic index is converted into the eye diagram data, y2(x) represents the corresponding preset expected index. Similarly, if the product parameters are sensitive to the key characteristic index, the characteristic index parameters can be used to decompose and refine the existing characteristic index. Finally, based on the above expected index, the margin space corresponding to each characteristic index is gradient calculated to obtain the gradient strength of each characteristic index.

[0026] Step S140 , sorting the gradient strengths corresponding to the link simulation parameter configuration information, screening out the target gradient strength based on the sorting result, and determining the link simulation parameter configuration information corresponding to the target gradient strength as the target parameter configuration.

[0027] Specifically, the gradient strengths are sorted, the target gradient strength is screened out according to the sorting result, and the link simulation parameter configuration information corresponding to the target gradient strength is determined as the target parameter configuration. In some preferred embodiments, the link simulation parameter configuration information corresponding to the maximum gradient strength is determined as the target parameter configuration.

[0028] Through steps S110 to S140, a suitable target simulation model is first screened out from the default simulation model, and an evaluation method for the simulation parameter configuration information of each link for verifying the initial simulation result of the link is determined. The margin space between the data of the characteristic indicator and the expected indicator is first constructed, and the gradient strength of each characteristic indicator is determined through parameter normalization and gradient calculation. The target gradient strength is screened based on the gradient strength, and the corresponding configuration is determined as the optimal target parameter configuration, thereby avoiding the problem that there is no systematic evaluation method for the simulation test results and it is difficult to obtain a more reliable configuration recommendation, and greatly improving the accuracy of the optimal configuration parameters.

[0029] In one embodiment, based on the preset signal basic characteristics of the model to be simulated in the link to be simulated, selecting a target simulation model corresponding to the link to be simulated from the default simulation model includes: Based on the basic characteristics of the signal, the corresponding initial simulation model is selected from the default simulation model, and the working condition information of each initial simulation model is extracted; According to the preset communication protocol corresponding to the link to be simulated, each initial simulation model is grouped to obtain at least one network group; wherein a network group includes at least two model types; The operating condition information between the initial simulation models corresponding to each model type in the network group is matched pairwise. If it is detected that there is a set of target operating condition information that matches successfully in each model type, the target simulation model corresponding to the target operating condition information is determined according to the matching result.

[0030] Specifically, based on the basic characteristics of the signal, the corresponding initial simulation model is screened out from the default simulation model, and the operating condition information of each initial simulation model is extracted, wherein the basic characteristics of the signal mainly include the signal group, signal transmission type, level voltage, etc. mentioned above. The operating condition information of the initial simulation model reflects the basic parameter information of the initial simulation model when it is working. The operating condition information can be determined by the input and output curves of the model. In some embodiments, the operating condition information can be defined as an x-dimensional and y-dimensional matrix, wherein the y-dimensional matrix is ​​a three-dimensional matrix, corresponding to matrix information of three dimensions of typical value, minimum value and maximum value, respectively. The three-dimensional information corresponds to parameter information under different operating conditions. The following Table 1-1 is the V / I curve information of a model in the simulation model, that is, the operating condition information table, wherein Voltage is the voltage information of the simulation model, I (typ) is the typical current information of the simulation model at a certain working voltage, I (min) is the minimum current information of the simulation model at a certain working voltage, and I (max) is the maximum current information of the simulation model at a certain working voltage.

[0031] Table 1-1

[0032] In this embodiment, there are no excessive restrictions on the screening method of the initial simulation model, and all methods that can be screened according to the basic characteristics of the signal should fall within the protection scope of this application.

[0033] Furthermore, according to the preset communication protocol corresponding to the link to be simulated, each initial simulation model is grouped to obtain multiple network groups, and a network group generally includes multiple network types. Taking an ideal CPU-memory bar system as an example, according to the existing communication protocol, it is assumed that the system is divided into 8 or 9 data network groups and 1 address command control group, wherein each group of data signals is divided into three types of models, including eight data line models, two clock signal models, and one mask signal model.

[0034] Finally, the working condition information between the initial simulation models corresponding to each model type in the network group is matched in pairs, so that the working condition information corresponding to each type of model in the same network group is consistent to meet the timing requirements. If it is detected that there is a set of target working condition information that matches successfully in each model type, that is, it is detected that there are models in each model type that can keep the working condition information consistent, then the target simulation model corresponding to the target working condition information is determined according to the matching result. Similarly, the model that fails to match successfully is directly deleted. Taking the above-mentioned CPU-memory bar system as an example, the possible initial simulation models of the data line model are abcd ef, the possible initial simulation models of the clock signal model are ABCDE, and the possible initial simulation models of the mask signal model are 12345. At this time, it is necessary to ensure that the working condition information of the data line model, mask signal model and clock signal model under the same configuration is consistent, that is, when it is detected that the working condition information of the data line model-mask signal model-clock signal model under the same configuration is consistent, the match is judged to be successful. Assuming that the target working condition information that is successfully matched is aA1, bB2, cC3, dD4, eE5, at this time, the f model in the data line model fails to match successfully, delete the f model, and do not configure and simulate the f model. The above target simulation models are: Among them, ST mi_s Represents an ordered model list of all signals in a network group, that is, a list of each target simulation model in the above network group, ST kmi_s Represents a single network ordered model. Models in the same network group are associated with each other. Their configuration properties and operating condition information must be consistent. The network group model type is generally equivalent to the single network model type in the network group.

[0035] Through this embodiment, the parameters of the signal network model in the same group can be guaranteed to be consistent according to the similarities and differences of the input and output curve parameters of the initial simulation model, and the normalization of the signal configuration can be further achieved.

[0036] In one embodiment, matching the operating condition information between the initial simulation models corresponding to each model type in the network group in pairs includes: Get the preset window constraints; In the time sequence of the operating condition information, dynamic programming calculations are performed pairwise between the operating condition information of different model types belonging to the same network group based on window constraints. The similarity results between the operating condition information of different model types are obtained according to the dynamic programming calculation results. When it is detected that the similarity of the operating condition information of the initial simulation models between each model type is greater than the preset similarity threshold, it is determined that the operating condition information of the initial simulation models corresponding to each model type matches.

[0037] Specifically, first, a window constraint is preset. In the standard dynamic time warping algorithm (Dynamic TimeWarping, DTW), all possible point pairs across sequences are considered, which may lead to high computational complexity and high redundancy. Therefore, the above-mentioned window constraint is introduced in this embodiment. The window constraint is obtained through the VI / VT curve. The length of the window constraint is a dynamic length, and the constraint is performed according to the obtained length. Taking Table 1-1 as an example, according to the three-dimensional information shown in Table 1-1, the number of rows represents the constraint length of the window. The window lengths of signal models of different manufacturers will be inconsistent, but the window lengths of the same type are basically the same.

[0038] Furthermore, in the time sequence of the operating condition information, based on the above-mentioned window constraints, dynamic programming calculations are performed on the operating condition information of different model types belonging to the same network group. Specifically, the x-dimensional matrix information of the operating condition information is analyzed, and the dynamic programming table is filled in. According to the properties of the optimal substructure and overlapping subproblems, all possible alignment paths and difference values ​​between the two operating condition information are gradually calculated, wherein the alignment path refers to the first column of data in Table 1-1 above, i.e., the voltage value; the difference value represents the similarity result between the operating condition information of each model type. When it is detected that the similarity between the operating condition information of the initial simulation models of each model type is greater than the similarity threshold, it is determined that the operating condition information between the initial simulation models corresponding to each model type matches. In summary, in this embodiment, the curve dynamic time warping algorithm is used to store intermediate results, which avoids repeated calculations and realizes model de-redundancy.

[0039] In one embodiment, after selecting the corresponding initial simulation model from the default simulation model based on the basic characteristics of the signal and extracting the working condition information of each initial simulation model, the method further includes: Based on the working condition information, the input and output categories of each initial simulation model are divided to obtain an input category model, an output category model and an input and output category model; For the input category model, the output category model and the input and output category model respectively, the corresponding target simulation model is selected from the default simulation model.

[0040] Specifically, after the initial simulation model is screened and obtained, and before grouping according to the preset communication protocol, the present application needs to first divide the initial simulation models according to their input and output categories, thereby obtaining an input category model, an output category model, and an input-output category model. In practical applications, the network signal corresponds to the model type of the transceiver chip, and its model types mainly include input, output, and I / O. Input is the receiving state, and its associated curves are the power clamping and ground clamping curves, that is, for the input type model, attention should be paid to the operating condition information of its power clamping and ground clamping curves; output is the sending state, and its associated curves are the pull-up and pull-down curves and the rising and falling edge curves, that is, for the output type model, attention should be paid to the operating condition information of its pull-up and pull-down curves and the rising and falling edge curves; similarly, I / O has both sending and receiving states. When the model type is I / O type, it is necessary to determine the input state or output state according to the transceiver state corresponding to the actual transmission type TT of the signal, and then detect the corresponding associated curve.

[0041] After completing the division of the input category model, the output category model and the input-output category model, subsequent dynamic programming calculations are performed on each of these three types separately to complete the matching of the corresponding working condition information, and the above-mentioned target simulation models are obtained respectively.

[0042] In this embodiment, before screening the target simulation model, it is preferred to classify the input signal, output signal, and input and output signal models. This is because the specific transmission properties of the signal cannot be accurately determined from the IBIS model, and it is necessary to obtain the actual transmission type of the signal through database information to achieve an effect consistent with the actual physical transmission of the signal.

[0043] In one embodiment, determining the gradient strength of the corresponding characteristic index according to the margin space includes: Traverse the feature indicators of each dimension and calculate the gradient value of each dimension respectively; The gradient values ​​corresponding to each dimension are converted into norm form to obtain the gradient strength corresponding to each dimension.

[0044] Specifically, the gradient of the feature index of each dimension is obtained by gradient calculation, and the gradient calculation satisfies the partial derivative relationship as follows: Among them, x and y represent the horizontal and vertical coordinate parameters of each characteristic index parameter, respectively, and z represents the normalized overall characteristic index parameter.

[0045] After calculating the gradient value of each dimension according to the above method, the gradient value corresponding to each dimension is converted into a norm form to obtain the gradient strength of each dimension: Among them, ||z|| 2i Represents the L2 norm of each feature index parameter, that is, the gradient strength of each dimension, i represents the feature index parameter index number, and t represents the number of feature indicators.

[0046] In one embodiment, sorting the gradient strengths corresponding to the link simulation parameter configuration information and selecting the target gradient strength based on the gradient strengths includes: Each gradient strength is sorted in order from small to large, and the maximum value among the sorted gradient strengths is determined as the target gradient strength.

[0047] Specifically, define the gradient strength of the multi-feature index parameter ||z||' 2i For ||z|| 2i An ordered list, sorted from small to large, after sorting by ||z||' 2i Compose a new list L j , for L j Sort and get the optimal parameters. The list and the optimal parameter selection satisfy: Among them, L j represents the list of gradient strengths of specific indicators under each configuration, j is the active device optimization parameter index, cn is the total type of active device optimization parameters, L opt To select the optimal parameter, select the independent variable item with the largest value after the list is sorted.

[0048] Through this embodiment, the maximum gradient strength is determined as the target gradient strength, thereby quantifying the evaluation method of the simulation device and effectively improving the optimization accuracy and optimization efficiency of the simulation device configuration.

[0049] In one embodiment, based on the preset signal basic characteristics of the model to be simulated in the link to be simulated, selecting a target simulation model corresponding to the link to be simulated from the default simulation model includes: Determine the basic signal corresponding to the model to be simulated; Defining a target signal set of basic signals, wherein the target signal set includes basic characteristics of signals corresponding to the model to be simulated; The target signal set is mapped and matched with the default signal characteristics corresponding to the default simulation model, and the target simulation model is obtained by screening.

[0050] Specifically, establishing a simulation signal database based on material coding can effectively identify high-speed signals in design data and provide data support for simulation automation design, wherein the above database includes the default simulation model described in this embodiment. In electronic product design, the above basic signals, i.e., high-speed signals, can be determined by network signal naming, but since the naming standards cannot be unified, it is difficult to accurately extract the above basic signals, wherein the above basic signals are important signals of the model to be simulated, such as DQS0_N, DQS0_P, DQ0-DQ7, DM0, etc. In this embodiment, the signal naming can be determined according to the pin of the material model during the load period, and the device material number and pin can be determined as fixed network information. For example, for high-speed signals, the main control devices such as ASIC and CPU can be obtained according to the network relationship mapping with the load device. The pin information of FPGA and CPLD can be used differently in different projects, and can be used as identification information through the relationship between FPGA and load devices.

[0051] Specifically, the simulation signal database for defining device materials mainly includes the following information, as shown in the following example: Among them, "New Part Number" represents the material code; "PIN Number" represents the pin number; "XNetName" represents the network name, which is used to indicate the original network name of the pin and match the network name in the actual project; "Net Type" represents the network type, which can be used to judge the subtype of the signal, and then different electrical templates can be applied to determine the simulation results; "Edgesampling" represents edge sampling, which can be used to judge the actual operating rate of the signal; "SignalType" represents the signal protocol type, and this field can be used to determine the signal type of this network. Depending on the simulation type, the field design here will be different. For different types of signal and power simulation, the classification field for the active device model can be added. The above mainly provides parameter configuration examples for signal links.

[0052] "BUS NAME" represents the network group name and is used to simulate the timing analysis of the network group. Since the same group of networks has different signal types, that is, different signal models, in order to ensure the consistency of timing during actual simulation tuning and product configuration, it is necessary to ensure the consistency of various types of model configurations; "TransmissionType" represents the signal transmission type. This field is used to determine the signal transmission method, which is verified with the "Model_type" field in MI, and the model characteristics are further classified and identified through the model input and output characteristics; "Level Voltage" represents the signal level voltage. This field is used to determine the identification and screening based on "[Voltage Range]" under multiple model conditions.

[0053] In summary, in this embodiment, by determining the basic signal of the model to be simulated, the accurate name of the IBIS model corresponding to the model to be simulated, that is, 1.2.03.01.03XXX in the above table, can be determined, so as to further obtain model information related to the IBIS model corresponding to the model to be simulated.

[0054] In this embodiment, the preset basic signals corresponding to the model to be simulated are first determined, which can be achieved by defining a basic signal set and a subset, that is, defining a target signal set of the basic signal: Among them, S represents the high-frequency and high-speed network signal set, that is, the above basic signal set, k represents any network group, n is the total number of networks in the network group, N k Represents each high-frequency network signal parameter set, Sk represents a single high-speed network signal and its associated information, Skr is the associated device bit number, Skp is the associated device material number, Skn is the associated device model name, Skpp is the device pin information, Ntt represents the transmission type of the signal in the database association, including bidirectional and unidirectional, Nlv represents the level voltage of the network in the database association, and Nbn represents the signal group to which the network belongs. The above basic signal characteristics generally include the transmission type Ntt of the signal in the above database association, the level voltage Nlv of the network in the database association, and the signal group Nbn to which the network belongs.

[0055] Map and match the target signal set and the default signal characteristics corresponding to the default simulation model, extract the high-speed network signal set S, obtain the corresponding model information through the network-associated pin information, and define the network-based model parameter information as follows: Among them, STkpii represents the model pin information associated with the network, STkmi represents the model or model list associated with the network, where T[Skp][Skn][Cms] corresponds to the model list, T[Skp][Skn][Cmd] corresponds to a single model, STkvr represents the level voltage information associated with the network, STkmt represents the transceiver characteristics associated with the network, STmi represents the model list of all signals in the group to which the network belongs, where a single network model is an unordered structure and there is no association between networks, and the total number of network models is the product of the number of network models of each single signal.

[0056] Based on the basic signal characteristics and default signal characteristics Ntt and STkmt, Nlv and STkvr, Nbn and STmi in the above two sets, mapping and matching are performed respectively, that is, by mapping and matching the network database data with the signal transmission type, level voltage, and signal group of the model of the network-associated main control chip, the target simulation model is screened. In practical applications, since some IBIS model files are large and too much information is obtained, the key information of the voltage range can be obtained through multi-threading combined with the block reading method, and the device model can be screened based on the signal level.

[0057] In summary, in this embodiment, a target signal set of basic signals is first established (which can be obtained through design projects in actual applications) and a target signal set corresponding to the basic signals are established. The target signal set includes preset basic signal characteristics (signal transmission type, level voltage, and signal group); then, the default simulation model and all model information corresponding to the default simulation model are determined according to the device pin information corresponding to the network, and the default signal characteristics of the default simulation model are compared with the above target signal set (the default signal characteristics are also the signal transmission type, level voltage, and signal group), thereby completing the screening of the target simulation model.

[0058] The present application also provides a preferred embodiment of a parameter configuration method for communication link simulation, Figure 2 The figure is a flow chart of a parameter configuration method in one embodiment.

[0059] Step S210, establish the parameters of the default simulation model. In this embodiment, the default simulation model is the IBIS model. The IBIS model has a file format specification that specifies the data format of each component. The IBIS model structure parameters are established. The specific process is as follows: First, define the IBIS structure as a dictionary structure: Among them, PartNum is the device material code, Component is the device attribute field information index of the IBIS model, CIi is the device information, which at least includes: device name CCN, package parameters CPAI, i.e. RLC parasitic parameters, pin information CPII, model selection information CMS, model information CM; Model is the model field information index of the IBIS model, MIj is the model information, which at least includes: level voltage MVR, model transceiver characteristics MMT, C_comp parameter MCC, rising edge MRI, falling edge MFA, pull-up curve MPU, pull-down curve MPD, power clamping curve MGC, ground clamping curve GC, etc.

[0060] Specifically: i.RLC parasitic parameters: chip package electrical parameters ii.Ccomp: Transistor junction capacitance iii. Pull-up and pull-down VI curve: The VI curve is the relationship between voltage and current. This curve involves two aspects. The first is the driving current of the transistor. This value is determined by the chip's own circuit and is available in the data sheet of some manufacturers. The other is the load resistance setting, because the pull-up curve has three items: maximum, typical, and minimum, which are set according to the load resistance. The load resistance will also be indicated in some manuals.

[0061] iv. Rising edge and falling edge curves: The VT curve is the relationship between voltage and time, representing the moment when the signal changes from a low level to a high level. In digital circuits and communication systems, the rising edge generally represents the process of the signal state changing from 0 (low level) to 1 (high level), that is, the starting edge of the signal. The falling edge curve is the opposite of the rising edge curve.

[0062] v. Power supply and ground clamping curve: VI curve is the relationship between voltage and current, representing the voltage and current relationship between the power supply and ground of the clamping diode.

[0063] Step S220, based on the basic signal corresponding to the model to be simulated, determine the target signal set of the basic signal, and map and match the basic signal set with the default signal features corresponding to the default simulation model to obtain an initial simulation model. The basic signal set mainly includes three characteristics: signal transmission type, level voltage and signal group.

[0064] Step S230, divide the initial simulation model to obtain input category model, output category model, and input-output category model, divide the above three models into network groups respectively, match the working condition information between the initial simulation models corresponding to each model type in the network group, and if it is detected that the target working condition information is successfully matched, determine the target simulation model corresponding to the target working condition information according to the matching result. Before performing the pairwise matching, if it is detected that there are too many models of one or more types, it is necessary to identify and remove the redundant models with the same configuration.

[0065] Step S240, determine the list of target simulation models after screening, and use the Cartesian formula to perform a direct product on the model configuration list to obtain the link simulation parameter configuration information. Specifically, since the signal transmission methods mainly include single drive-single load and single drive-multiple load structures, and the configuration methods between single drives and between single loads and multiple loads are consistent, and in order to adapt the chip configuration to multiple projects, the configuration of each device of multiple loads generally adopts the same configuration. In this embodiment, the simulation signal data information can be obtained through the load device material number, and the network signal type can be determined according to the pin signal type of the load device, and the signal link receiving and sending method can be determined. The configuration of each active chip satisfies the Cartesian product formula and satisfies: Wherein: Ai represents the ordered model of the sending-end device of the signal network group, and Bi represents the ordered model of the receiving-end device of the signal network group; when i=0, A0 represents the configuration of the main control chip during forward transmission, and B0 represents the configuration of the load chip during forward transmission; when i=1, A1 represents the configuration of the main control chip during reverse transmission, and B1 represents the configuration of the load chip during reverse transmission.

[0066] Step S250, based on the link simulation parameter configuration information, a simulation waveform is calculated. The simulation waveform is generally a time domain simulation waveform. The time domain simulation waveform curve is as follows: Figure 3 shown. Figure 3 It is a simulation waveform of the above-mentioned screened target simulation model and the corresponding link simulation parameter configuration information. It can be seen from the figure that it is a voltage value time series diagram, that is, the change of voltage value in the time dimension. Those skilled in the art can understand that calculating the simulation waveform corresponding to the target simulation model and the link simulation parameter configuration information is a prior art, such as obtaining a predicted simulation waveform based on simulation software; the output corresponding time domain waveform curve of the signal link can also be calculated according to the voltage-current relationship of the target simulation model, that is, the above-mentioned simulation waveform.

[0067] Step S260, extracting characteristic indicators based on the above time domain simulation waveform, and calculating the margin space of the characteristic indicators, wherein the multi-dimensional characteristic indicators are as follows: Si = [EP BT BER SNR …] i=1,2,...t; Among them: EP represents the eye diagram feature, BT represents the bathtub curve feature, BER represents the bit error rate, and SNR represents the signal-to-noise ratio feature. S is a multi-dimensional parameter feature parameter. In practical applications, it is mostly four-dimensional parameter data. The feature index can be increased or decreased according to the actual working conditions. When a comprehensive evaluation of multiple feature index parameters is required, the weighted average can be calculated.

[0068] Step S270, performing gradient calculation on the margin space, sorting the gradient strengths corresponding to the link simulation parameter configuration information, screening out the target gradient strength, and determining the link simulation parameter configuration information corresponding to the target gradient strength as the target parameter configuration.

[0069] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0070] Based on the same inventive concept, the embodiment of the present application also provides a parameter configuration device for implementing the parameter configuration method involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more parameter configuration device embodiments provided below can refer to the limitations on the parameter configuration method above, and will not be repeated here.

[0071] In one embodiment, Figure 4 As shown, a parameter configuration device is provided, including: an acquisition module 41, a calculation module 42 and a generation module 43, wherein: The acquisition module 41 is used to select a target simulation model corresponding to the link to be simulated from the default simulation model based on the preset signal basic characteristics of the model to be simulated in the link to be simulated, and simulate the link to be simulated based on the target simulation model to obtain the corresponding initial simulation result of the link; The calculation module 42 is used to determine all parameter configuration information of the target simulation model corresponding to the initial simulation result of the link, and determine at least one set of link simulation parameter configuration information according to the parameter configuration information; extract characteristic indicators of the link simulation parameter configuration information, calculate the margin space corresponding to the characteristic indicator based on the data of the characteristic indicator and the preset expected indicator, and determine the gradient strength of the corresponding characteristic indicator according to the margin space; The generating module 43 is used to sort the gradient strengths corresponding to the link simulation parameter configuration information, screen out the target gradient strength based on the sorting result, and determine the link simulation parameter configuration information corresponding to the target gradient strength as the target parameter configuration.

[0072] Each module in the above parameter configuration device can be implemented in whole or in part by software, hardware and a combination thereof. Each module can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to each module above.

[0073] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 5 As shown. The computer device includes a processor, a memory and a network interface connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store data related to parameter configuration. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a parameter configuration method is implemented.

[0074] Those skilled in the art will understand that Figure 5 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0075] In one embodiment, a computer readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented: Based on the preset signal basic characteristics of the to-be-simulated model in the to-be-simulated link, a target simulation model corresponding to the to-be-simulated link is selected from the default simulation model, and the to-be-simulated link is simulated based on the target simulation model to obtain the corresponding initial simulation result of the link; Determine all parameter configuration information of the target simulation model corresponding to the initial simulation result of the link, and determine at least one set of link simulation parameter configuration information according to the parameter configuration information; Extracting characteristic indicators of link simulation parameter configuration information, calculating a margin space corresponding to the characteristic indicator based on data of the characteristic indicator and a preset expected indicator, and determining a gradient strength of the corresponding characteristic indicator according to the margin space; The gradient strengths corresponding to the link simulation parameter configuration information are sorted, the target gradient strength is screened out based on the sorting result, and the link simulation parameter configuration information corresponding to the target gradient strength is determined as the target parameter configuration.

[0076] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties.

[0077] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited to this.

[0078] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0079] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A parameter configuration method for communication link simulation, characterized in that: The method comprises: Based on the preset signal basic characteristics of the model to be simulated in the link to be simulated, a target simulation model corresponding to the link to be simulated is selected from the default simulation model, and the link to be simulated is simulated based on the target simulation model to obtain a corresponding initial simulation result of the link; Determine all parameter configuration information of the target simulation model corresponding to the initial link simulation result, and determine at least one set of link simulation parameter configuration information according to the parameter configuration information; Extracting characteristic indicators of the link simulation parameter configuration information, calculating a margin space corresponding to the characteristic indicator based on data of the characteristic indicator and a preset expected indicator, and determining a gradient strength of the corresponding characteristic indicator according to the margin space; The gradient strengths corresponding to the link simulation parameter configuration information are sorted, a target gradient strength is screened out based on the sorting result, and the link simulation parameter configuration information corresponding to the target gradient strength is determined as a target parameter configuration.

2. The method according to claim 1, characterized in that: The step of selecting a target simulation model corresponding to the link to be simulated from a default simulation model based on a preset signal basic feature of a model to be simulated in the link to be simulated comprises: Based on the basic characteristics of the signal, a corresponding initial simulation model is selected from the default simulation model, and working condition information of each initial simulation model is extracted; According to the preset communication protocol corresponding to the link to be simulated, each of the initial simulation models is grouped to obtain at least one network group; wherein one of the network groups includes at least two model types; The operating condition information between the initial simulation models corresponding to each of the model types in the network group is matched pairwise. If it is detected that there is a set of target operating condition information that matches successfully in each of the model types, the target simulation model corresponding to the target operating condition information is determined based on the matching result.

3. The method according to claim 2, characterized in that The pairwise matching of the operating condition information between the initial simulation models corresponding to the respective model types in the network group includes: Get the preset window constraints; In the time sequence of the operating condition information, dynamic programming calculations are performed on each of the operating condition information of different model types belonging to the same network group based on the window constraints, and the similarity results between the operating condition information of different model types are obtained according to the dynamic programming calculation results. When it is detected that the similarity of the operating condition information of the initial simulation model between each model type is greater than a preset similarity threshold, it is determined that the operating condition information of the initial simulation models corresponding to each model type matches.

4. The method according to claim 2, characterized in that: After selecting the corresponding initial simulation model from the default simulation model based on the basic characteristics of the signal and extracting the working condition information of each of the initial simulation models, the method further includes: Based on the working condition information, the input and output categories of each of the initial simulation models are divided to obtain an input category model, an output category model and an input-output category model; For the input category model, the output category model and the input-output category model respectively, the corresponding target simulation model is selected from the default simulation model.

5. The method according to claim 1, characterized in that The step of determining the gradient strength of the corresponding characteristic index according to the margin space includes: Traversing the characteristic index of each dimension, and calculating the gradient value of each dimension respectively; The gradient values ​​corresponding to each dimension are converted into a norm form to obtain the gradient strength corresponding to each dimension.

6. The method according to claim 1, characterized in that The step of sorting the gradient strengths corresponding to the link simulation parameter configuration information and selecting a target gradient strength based on the gradient strengths includes: Each of the gradient strengths is sorted in order from small to large, and the maximum value of the sorted gradient strengths is determined as the target gradient strength.

7. The method according to claim 1, characterized in that The step of selecting a target simulation model corresponding to the link to be simulated from a default simulation model based on a preset signal basic feature of a model to be simulated in the link to be simulated comprises: Determining a basic signal corresponding to the model to be simulated; Defining a target signal set of the basic signal, wherein the target signal set includes the basic characteristics of the signal corresponding to the model to be simulated; The target signal set is mapped and matched with the default signal characteristics corresponding to the default simulation model, and the target simulation model is obtained by screening.

8. A parameter configuration device, characterized in that: The device comprises: An acquisition module is used to select a target simulation model corresponding to the link to be simulated from the default simulation model based on the preset signal basic characteristics of the model to be simulated in the link to be simulated, and simulate the link to be simulated based on the target simulation model to obtain the corresponding link initial simulation result; A calculation module, used to determine all parameter configuration information of the target simulation model corresponding to the initial simulation result of the link, and determine at least one set of link simulation parameter configuration information according to the parameter configuration information; extract characteristic indicators of the link simulation parameter configuration information, calculate the margin space corresponding to the characteristic indicator based on the data of the characteristic indicator and the preset expected indicator, and determine the gradient strength of the corresponding characteristic indicator according to the margin space; A generating module is used to sort the gradient strengths corresponding to the link simulation parameter configuration information, screen out a target gradient strength based on the sorting result, and determine the link simulation parameter configuration information corresponding to the target gradient strength as the target parameter configuration.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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