CN115048235B - Configuration method, device, equipment and medium of link parameters - Google Patents
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Abstract
The disclosure provides a configuration method, device, equipment, medium, program product and chip of link parameters, relates to the technical field of computers, and particularly relates to high-speed serial transmission and chip technology. The specific implementation scheme is as follows: obtaining a training parameter list of a target data path obtained through link training, wherein the training parameter list comprises a plurality of groups of link parameter values; responding to the error rate on the target data path being higher than a preset threshold value, traversing the training parameter list, and selecting a first target link parameter value from the training parameter list; and configuring the target data path according to the first target link parameter value. The method and the device can improve the response speed of the high-speed serial transmission link, improve the transmission quality of the link and improve the stability and the reliability of the link.
Description
Technical Field
The present disclosure relates to the field of computer technology, and in particular, to a method, an apparatus, a device, a medium, a program product, and a chip for configuring a link parameter.
Background
Currently, in the design of high-speed digital transmission systems, high-speed serial transmission structures are mostly used, such as PCIe (peripheral component interconnect express, high-speed serial computer expansion bus standard), SAS (Serial Attached SCSI, serial attached SCSI interface)/SATA (Serial Advanced Technology Attachment ), USB 3.0 (Universal Serial Bus, universal serial bus), and the like. With the increase of transmission rate, the problem of signal integrity of the transmission link has become a major problem that hinders the design of high-speed serial transmission links.
In order to solve this problem, de-emphasis and equalization techniques are commonly used in high-speed serial transmission structures to compensate for the different loss of the link to the high-low frequency signals. Meanwhile, in order to adapt to the requirements of different transmission link environments, link training technology is adopted to adaptively adjust equalization parameter values. Retraining is carried out when the bit errors occur in the link, and parameters are corrected, so that the purpose of repairing the link errors is achieved.
However, the above prior art still cannot completely match the environmental requirements of link transmission, so that instability of the link is caused, and serious problems such as loss of system equipment or service interruption occur.
Disclosure of Invention
The present disclosure provides a method, apparatus, device, medium, program product, and chip for configuring link parameters.
According to an aspect of the present disclosure, there is provided a method for configuring link parameters, including:
obtaining a training parameter list of a target data path obtained through link training, wherein the training parameter list comprises a plurality of groups of link parameter values;
responding to the error rate on the target data path being higher than a preset threshold value, traversing the training parameter list, and selecting a first target link parameter value from the training parameter list;
and configuring the target data path according to the first target link parameter value.
According to another aspect of the present disclosure, there is provided a configuration apparatus of link parameters, including:
the system comprises a training parameter list acquisition module, a link training module and a link training module, wherein the training parameter list is used for acquiring a training parameter list of a target data path obtained through link training, and the training parameter list comprises a plurality of groups of link parameter values;
the training parameter list traversing module is used for traversing the training parameter list and selecting a first target link parameter value from the training parameter list in response to the error rate on the target data channel being higher than a preset threshold;
And the link parameter configuration module is used for configuring the target data path according to the first target link parameter value.
According to another aspect of the present disclosure, there is provided an electronic device including:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of configuring link parameters according to any embodiment of the present disclosure.
According to another aspect of the present disclosure, there is provided a non-transitory computer-readable storage medium storing computer instructions for causing a computer to perform the method of configuring link parameters according to any embodiment of the present disclosure.
According to another aspect of the disclosure, a chip is provided, including a high-speed serial transmission system, where the high-speed serial transmission system includes a plurality of data paths, each data path corresponds to a receiving end and a transmitting end, and the receiving end is configured to perform link training on the data path corresponding to the receiving end; wherein,,
The high-speed serial transmission system further comprises a link parameter configuration module, which is specifically used for:
obtaining a training parameter list of a target data path obtained through link training through a receiving end of any target data path, wherein the training parameter list comprises a plurality of groups of link parameter values;
responding to the error rate on the target data path being higher than a preset threshold value, traversing the training parameter list, and selecting a first target link parameter value from the training parameter list;
and configuring the target data path according to the first target link parameter value.
It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the disclosure, nor is it intended to be used to limit the scope of the disclosure. Other features of the present disclosure will become apparent from the following specification.
Drawings
The drawings are for a better understanding of the present solution and are not to be construed as limiting the present disclosure. Wherein:
fig. 1 is a schematic diagram of a method of configuring link parameters according to an embodiment of the present disclosure;
fig. 2 is a schematic diagram of a method of configuring link parameters according to an embodiment of the present disclosure;
fig. 3 is a schematic diagram of a method of configuring link parameters according to an embodiment of the present disclosure;
Fig. 4 is a schematic diagram of a method of configuring link parameters according to an embodiment of the present disclosure;
fig. 5 is a schematic diagram of a method of configuring link parameters according to an embodiment of the present disclosure;
fig. 6 is a schematic diagram of a method of configuring link parameters according to an embodiment of the present disclosure;
fig. 7 is a schematic diagram of a configuration apparatus of link parameters according to an embodiment of the present disclosure;
fig. 8 is a block diagram of an electronic device for implementing a method of configuring link parameters of an embodiment of the present disclosure.
Description of the embodiments
Exemplary embodiments of the present disclosure are described below in conjunction with the accompanying drawings, which include various details of the embodiments of the present disclosure to facilitate understanding, and should be considered as merely exemplary. Accordingly, one of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the present disclosure. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
It should be noted that the terms "first," "second," and the like in the description and claims of the present disclosure and in the foregoing figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the disclosure described herein may be capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Fig. 1 is a flow chart of a configuration method of link parameters according to an embodiment of the disclosure, which is applicable to a case of configuring link parameters for a data path in a high-speed serial transmission system, and relates to the field of computer technology, in particular to high-speed serial transmission and chip technology. The method may be performed by a configuration means of link parameters, which is implemented in software and/or hardware, preferably in an electronic device, such as a computer device or a mobile terminal. In addition, the device can be configured in a chip, such as a voice processing chip. As shown in fig. 1, the method specifically includes the following steps:
s101, obtaining a training parameter list of a target data path obtained through link training, wherein the training parameter list comprises a plurality of groups of link parameter values.
In general, a high-speed serial transmission system includes a plurality of data links, each of which includes a plurality of data paths, each of which corresponds to one receiving side and one transmitting side. The receiving end is used for receiving data from the outside, calculating the error rate of the data path, and when the error rate is higher than a preset threshold value, carrying out link training on the data path corresponding to the error rate so as to determine and adjust the link parameters.
The link parameters on the data path include three types: the data path corresponds to the parameters of the Transmitter Equalization (transmit equalization) unit on the transmit side for processing the signal de-emphasis, the Automatic Generation Control (automatic power generation control) unit on the receive side for linearly adjusting the received signal amplitude, and the parameters of the continuous-time linear equalizer Continuous time linear equalizer on the receive side.
When the link training is performed, the receiving end obtains multiple sets of link parameter values, each set of link parameter values includes the value of each link parameter, and the values of the same link parameter can be the same or different between different sets of link parameter values, that is, the different sets of link parameter values are the combination of different values of each link parameter. The link training is performed to find an optimal set of link parameter values to improve the bit error rate. Therefore, the multiple sets of link parameter values in the training parameter list are link parameter candidates obtained in the training process.
S102, traversing the training parameter list and selecting a first target link parameter value from the training parameter list in response to the error rate on the target data path being higher than a preset threshold.
S103, configuring a target data path according to the first target link parameter value.
The receiving end calculates and monitors the error rate on the data path, when the error rate is higher than a preset threshold, the embodiment of the disclosure traverses the training parameter list, selects a first target link parameter value from the training parameter list, and configures the target data path according to the first target link parameter value. For example, another set of link parameter values, different from the currently used link parameter values, is selected from the training parameter list as the first target link parameter value.
It should be noted that in the prior art, link training is generally performed again whenever the error rate is higher than the threshold value, so as to obtain the optimal link parameter value for configuration. However, the link suffers interference in a short time to cause error rate abnormality, but it takes time to re-train the link, so that the data path cannot quickly respond to such a short-time change. Moreover, the link training is realized by adopting a short-time fixed code stream, and the link training is different from the actual link transmission, so that the random code stream condition of the actual transmission cannot be completely reflected, and therefore, the optimal solution obtained through retraining cannot always meet the transmission requirement in the current environment, and the better signal transmission performance cannot be quickly achieved.
In the technical scheme of the embodiment of the disclosure, the result of the link training is fully used, and when the error rate does not meet the threshold requirement, the link training is not performed again as in the prior art, but the link parameters are configured by utilizing a plurality of groups of link parameter values obtained by the last training. The method aims to solve the problem of slow response speed caused by each time of link training, and to utilize the parameters acquired in the history training, so that the parameters currently meeting the link transmission can be found out at the fastest response speed, thereby improving the stability and reliability of the transmission link.
Fig. 2 is a flow chart of a method for configuring link parameters according to an embodiment of the present disclosure, which is further optimized based on the above embodiment. As shown in fig. 2, the method specifically includes the following steps:
s201, obtaining a training parameter list of a target data path obtained through link training, wherein the training parameter list comprises a plurality of groups of link parameter values.
The target data path can be used for high-speed serial data transmission of the chip. The training parameter list may be obtained through a specific control port provided by the receiving end of the target data path.
S202, traversing the training parameter list and selecting a first target link parameter value from the training parameter list in response to the error rate on the target data path being higher than a preset threshold.
S203, configuring a target data path according to the first target link parameter value.
S204, monitoring the error rate of the target data path according to the preset time interval.
Wherein, the preset time interval may be configured according to circumstances, which is not limited in any way by the embodiments of the present disclosure.
S205, judging whether the error rate exceeds a preset threshold, if not, returning to S204, and if so, executing S206.
S206, judging whether the training parameter list of the target data path is completely traversed, and if not, executing S207.
S207, selecting a second target link parameter value from the training parameter list of the target data path.
S208, configuring a target data path according to the second target link parameter value, and continuously monitoring the error rate.
Specifically, due to the influence of factors such as environment, the error rate of the data path needs to be monitored according to a certain time interval. After the receiving end calculates the error rate, judging whether the error rate exceeds a preset threshold value, and if the error rate does not exceed the threshold value, not needing to be processed. If the threshold is exceeded, the link parameters of the data path need to be readjusted. The calculation method of the error rate belongs to the prior art, and is not repeated here.
The training parameter list obtained through link training comprises a plurality of groups of link parameter values, and if the error rate is detected to exceed the threshold value each time, another link parameter value different from the link parameter value used on the current channel can be selected from the training parameter list for configuration. In the embodiment of the disclosure, after the target data path is configured according to the first target link parameter value, the target data path performs data transceiving based on the first target link parameter value. When the error rate exceeds the threshold again, it is first determined whether the training parameter list is traversed completely, that is, whether a plurality of sets of link parameter values in the training parameter list have been selected, and the target data path is configured according to the selected link parameter values. If the judgment is negative, the fact that the link parameter value which is not selected and configured exists is indicated, the link parameter value is used as a second target link parameter value, and the target data path is configured according to the second target link parameter value. And then, the target data path carries out data receiving and transmitting based on the configured second target link parameter value, and the error rate is continuously monitored. Similarly, in the subsequent monitoring process, if the error rate exceeds the threshold again and the training parameter list is not completely traversed, a group of link parameter values which are not traversed yet are selected from the training parameter list again, and the process is similar to the process of selecting the second target link parameter value, so that the description is omitted.
According to the technical scheme, in the process of monitoring the error rate, when the error rate exceeds the preset threshold, link training is not carried out again as in the prior art, the result of the link training is fully used, and as long as a plurality of groups of link parameter values in a training parameter list obtained through historical training are not completely traversed, a group of link parameter values are selected to carry out link parameter configuration on a data path. Therefore, the embodiment of the disclosure can avoid the problem of slow response speed caused by each time of link training, and simultaneously, the parameters acquired in the history training are utilized, so that the parameters currently meeting the link transmission can be found out at the fastest response speed, thereby improving the stability and reliability of the transmission link.
Fig. 3 is a flow chart of a method for configuring link parameters according to an embodiment of the present disclosure, which is further optimized based on the above embodiment. As shown in fig. 3, the method specifically includes the following steps:
s301, obtaining a training parameter list of a target data path obtained through link training, wherein the training parameter list comprises a plurality of groups of link parameter values.
S302, traversing the training parameter list and selecting a first target link parameter value from the training parameter list in response to the error rate on the target data path being higher than a preset threshold.
S303, configuring a target data path according to the first target link parameter value.
S304, monitoring the error rate on the target data path according to the preset time interval.
S305, judging whether the error rate exceeds a preset threshold, if not, continuing monitoring, and if so, executing S306.
S306, judging whether the training parameter list of the target data path is completely traversed, and if yes, executing S307.
S307, acquiring a training parameter list of the adjacent data paths of the target data path.
In the embodiment of the disclosure, when the training parameter list of the target data path has been traversed completely, whether the target data path has an adjacent data path is first determined, and if so, the training parameter list of the adjacent data path of the target data path is obtained, so that link parameter configuration is performed on the target data path according to the training parameter list of the adjacent data path.
Specifically, the high-speed serial transmission system includes a plurality of data links, each data link includes a plurality of data paths, for example, one data link includes four data paths, namely, data paths 1-4, respectively, and then the data path 1 and the data path 2 are adjacent data paths. Similarly, each of the data paths 1 and 4 has one adjacent data path, and each of the data paths 2 and 3 has two adjacent data paths. In an actual transmission link, however, it is generally required that the routing layout of the data paths in the same direction in the same data link is very similar for a high-speed serial transmission system having a plurality of data paths, and the data paths all have the same link environment. Therefore, the link parameters of the current target data path can largely adopt the link parameter values of the data paths adjacent to the current target data path, and better link transmission effect can be achieved. Therefore, the embodiment of the disclosure utilizes the historical training results of the adjacent data paths to configure the link parameters of the current data path, and can achieve the purpose of rapidly improving the stability and reliability of the transmission link while responding more rapidly.
S308, judging whether the training parameter list of the adjacent data paths is traversed or not, if not, executing S309 and S310, returning to S304, and if yes, executing S311.
S309, selecting a third target link parameter value from the training parameter list of the adjacent data paths.
S310, configuring a target data path according to the third target link parameter value, and continuously monitoring the error rate.
S311, performing link training on the target data path again.
If the training parameter list of the adjacent data path is not completely traversed, a third target link parameter value can be selected from the training parameter list, if the training parameter list of the adjacent data path is completely traversed, the link training is performed on the target data path again, and the link training is performed again, so that the link parameter value meeting the link transmission stability can be ensured to be determined as a spam scheme. After the target data path is configured according to the third target link parameter value, the target data path performs data transceiving based on the third target link parameter value, then continues to detect the error rate, and if the error rate exceeds the threshold value again, selects another group of link parameter values from the training parameter list of the adjacent data path again for configuration until the training parameter list of the adjacent data path is traversed completely, and then performs link training. It should be noted that, after the link training is performed again, the embodiments of the present disclosure may update the training parameter list of the target data path, and then repeatedly perform the method according to the embodiments of the present disclosure based on the updated training parameter list.
According to the technical scheme of the embodiment of the disclosure, each time of link training results are fully used, when the error rate does not meet the threshold requirement, the link training is not performed again as in the prior art, and the link parameters of the data path are configured by utilizing a plurality of groups of link parameter values in the training parameter list obtained by the last training. And further, when the training parameter list is traversed and the error rate still does not meet the requirement, the configuration of the link parameters is also performed according to the training parameter list of the adjacent data paths. The method aims to solve the problem of slow response speed caused by each time of link training, and to utilize the parameters acquired in the history training, so that the parameters currently meeting the link transmission can be found out at the fastest response speed, thereby improving the stability and reliability of the transmission link. Finally, a bottom-covering scheme is provided, and when the two modes can not meet the requirement on the error rate, link training is performed again, so that the stability and the reliability of the high-speed transmission link are further ensured.
Fig. 4 is a flow chart of a method for configuring link parameters according to an embodiment of the present disclosure, which is further optimized based on the above embodiment. As shown in fig. 4, the method specifically includes the following steps:
S401, obtaining a training parameter list of a target data path obtained through link training, wherein the training parameter list comprises a plurality of quality factor values and link parameter values corresponding to the quality factor values, the quality factor values are used for calibrating the quality of transmission data on the target data path, and the quality factor values are calculated according to the corresponding link parameter values.
Specifically, in the link training process, the receiving end corresponding to the target data path generally calculates a quality factor value according to the combination of different link parameter values, and the quality factor value is used to measure the quality of the transmission data on the target data path. Therefore, a plurality of quality factor values and link parameter values corresponding to the respective quality factor values can be obtained.
And S402, traversing the training parameter list and determining a target quality factor value from the training parameter list in response to the error rate on the target data channel being higher than a preset threshold.
S403, taking a link parameter value corresponding to the target quality factor value in the training parameter list as a first target link parameter value.
In the prior art, a set of link parameter values corresponding to the maximum quality factor value is generally selected for configuration through link training. However, the calculated quality factor value is calculated based on the current environment, and has a certain limitation, that is, the maximum quality factor value obtained by the link training is not necessarily suitable for the changed environment, so that the best signal transmission performance still cannot be achieved. And the multiple groups of link parameter values in the training parameter list can be suitable for current link transmission to a great extent. Therefore, when a set of link parameter values corresponding to the maximum quality factor value is used for data transmission, if the error rate exceeds the threshold value, the embodiment of the disclosure first traverses the training parameter list, determines the target quality factor value from the training parameter list, and uses the link parameter value corresponding to the target quality factor value in the training parameter list as the first target link parameter value, so as to find the parameter currently meeting the link transmission at the fastest response speed, thereby improving the stability and reliability of the transmission link.
S404, configuring a target data path according to the first target link parameter value.
According to the technical scheme, in the process of monitoring the error rate, when the error rate exceeds the preset threshold, link training is not carried out again as in the prior art, a result of the link training is fully used, and a group of link parameter values are selected from a training parameter list to carry out link parameter configuration on a data path. Therefore, the embodiment of the disclosure can avoid the problem of slow response speed caused by each time of link training, and simultaneously, the parameters acquired in the history training are utilized, so that the parameters currently meeting the link transmission can be found out at the fastest response speed, thereby improving the stability and reliability of the transmission link.
Fig. 5 is a flow chart of a method for configuring link parameters according to an embodiment of the present disclosure, which is further optimized based on the above embodiment. As shown in fig. 5, the method specifically includes the following steps:
s501, obtaining a training parameter list of a target data path obtained through link training, wherein the training parameter list comprises a plurality of quality factor values and link parameter values corresponding to the quality factor values, the quality factor values are used for calibrating the quality of transmission data on the target data path, and the quality factor values are calculated according to the corresponding link parameter values.
S502, determining a current quality factor value corresponding to a current link parameter value configured on a target data path in response to the error rate on the target data path being higher than a preset threshold.
S503, sorting the quality factor values in the training parameter list in descending order.
S504, traversing the training parameter list according to the descending order, and taking the next quality factor value smaller than the current quality factor value in the training parameter list as a target quality factor value.
Specifically, in the prior art, a maximum quality factor value is usually obtained through link training, and then a link parameter corresponding to the maximum quality factor value is configured on a data path. In the embodiment of the disclosure, each quality factor value in the training parameter list may be sorted in descending order, and then when the error rate is higher than the preset threshold, the training parameter list is traversed in descending order, and the next quality factor value in the training parameter list smaller than the current quality factor value is used as the target quality factor value. The next quality factor value smaller than the current quality factor value can be used as a sub-quality factor value, and the link parameter value corresponding to the sub-quality factor value is the sub-optimal link parameter value.
S505, taking a link parameter value corresponding to the target quality factor value in the training parameter list as a first target link parameter value.
S506, configuring a target data path according to the first target link parameter value.
In one embodiment, the training parameter list includes 5 quality factor values, which are sorted in descending order to be quality factor values 1-5, respectively. After the first link training, the target data path is configured according to the maximum quality factor value 1. When the error rate exceeds the threshold value for the first time, the link parameter value corresponding to the suboptimal quality factor value 2 is selected for configuration by traversing the training parameter list, and then data transmission and reception of the data path are carried out based on the link parameter value. However, if the requirement for the error rate is still not satisfied at this time, the next-best quality factor value continues to be selected for configuration. It is assumed that the bit error rate of the data path is not below the threshold until the suboptimal quality factor value 3 is selected, meeting the requirements. Then the next time the bit error rate is above the threshold value, the traversal may be performed in the order of the quality factor values 4, 5, 1, 2. That is, in the training parameter list, the figure of merit value that has not been traversed or tried is preferentially selected as the sub-figure of merit value, and the figure of merit value that has been traversed may also be tried again, but with a lower priority than the figure of merit value that has not been tried. If all the quality factor values are traversed or tried and the data transmission is performed based on the corresponding link parameter values, the current error rate requirement can be met, and when the error rate exceeds the threshold again, the current training parameter list does not need to be traversed again.
According to the technical scheme, in the process of monitoring the error rate, when the error rate exceeds the preset threshold, link training is not carried out again as in the prior art, a result of the link training is fully used, and a group of suboptimal link parameter values different from the current configuration are selected from a training parameter list to carry out link parameter configuration on a data path. Therefore, the embodiment of the disclosure can avoid the problem of slow response speed caused by each time of link training, and simultaneously, the parameters acquired in the history training are utilized, so that the parameters currently meeting the link transmission can be found out at the fastest response speed, thereby improving the stability and reliability of the transmission link.
Fig. 6 is a flow chart of a method for configuring link parameters according to an embodiment of the present disclosure, which is further optimized based on the above embodiment. As shown in fig. 6, the method specifically includes the following steps:
s601, powering up the system and performing first link training.
The system refers to a high-speed serial transmission system, such as a high-speed serial transmission system in a voice chip. After the system is electrified, a training parameter list corresponding to each data path can be obtained through first link training. In this embodiment, a configuration method of the link parameters is described by taking a target data path as an example.
S602, obtaining a training parameter list of a target data path obtained through link training.
The training parameter list includes a plurality of quality factor values FOM (Figure of Merit) and link parameter values corresponding to the quality factor values, where the quality factor values are used to calibrate the quality of the data transmitted on the target data path, and each quality factor value is calculated according to the corresponding link parameter value.
S603, sorting the training parameter list in descending order according to the FOM value.
S604, carrying out link parameter configuration by using a group of parameters with the maximum FOM value.
That is, a set of link parameter values corresponding to the maximum FOM value is selected from the training parameter list for link parameter configuration.
S605, error detection is carried out, and the error rate is calculated.
S606, judging whether the error rate is larger than a threshold value, if yes, executing S607, otherwise returning to executing S605.
S607, judging whether the training parameter list is traversed completely, if not, executing S608, continuing to execute S605, and if yes, executing S609.
S608, selecting a suboptimal link parameter value from the training parameter list to carry out link parameter configuration.
The link parameter values corresponding to the FOM values smaller than the current FOM value may be selected as the sub-optimal link parameter values from the training parameter list sorted in descending order. After the configuration is finished, data receiving and transmitting are carried out based on the current configuration, and error rate monitoring is continued.
S609, judging whether the training parameter list of the adjacent data paths is completely traversed, if yes, executing S610 and S611, returning to S603, otherwise executing S612, returning to S605.
If the data link where the target data path is located has a data path adjacent to the target data path, carrying out link parameter configuration on the current target data path according to the training parameter list of the adjacent data path.
S610, performing link training on the target data path again.
S611, updating a training parameter list of the target data path.
If the training parameter list of the adjacent data path is traversed completely, no suboptimal parameter is indicated for selection, at this time, link training can be performed again, and the training parameter list is updated, so that when the error rate exceeds the threshold again, suboptimal parameters are selected again for configuration according to the updated training parameter list.
S612, selecting link parameters from the training parameter list of the adjacent data paths to configure.
If the training parameter list of the adjacent data path is not completely traversed, selecting link parameters from the training parameter list of the adjacent data path for configuration. Specifically, sub-optimal link parameters different from the current configuration parameters can be selected from the training parameter list of the adjacent data paths for reconfiguration, and the error rate is continuously monitored.
According to the technical scheme of the embodiment of the disclosure, each time of link training results are fully used, when the error rate does not meet the threshold requirement, the link training is not performed again as in the prior art, and the link parameters are configured by utilizing a plurality of groups of FOM values obtained by the last training. And further, when the multiple sets of FOM values are all tried and the error rate still does not meet the requirement, the configuration of the link parameters is also performed according to the FOM values of the adjacent channels. The purpose of this is to avoid the problem of slow response speed caused by each link training. Meanwhile, even if retraining is performed again, since each FOM value is calculated based on the current environment, the optimal FOM obtained by retraining is not necessarily suitable for the changed environment, so that the optimal signal transmission performance still cannot be achieved. The embodiment of the disclosure utilizes the parameters obtained in the history training, and can find the parameters currently meeting the link transmission with the fastest response speed, thereby improving the stability and reliability of the transmission link. Finally, a bottom-covering scheme is provided, when the two modes cannot meet the requirement on the error rate, retraining is carried out, and a training parameter list is updated, so that the stability and the reliability of the high-speed transmission link are further ensured.
Fig. 7 is a schematic diagram of a configuration apparatus for link parameters according to an embodiment of the present disclosure, which is applicable to a case of configuring link parameters for a data path in a high-speed serial transmission system, and relates to the field of computer technology, in particular to high-speed serial transmission and chip technology. The device can realize the configuration method of the link parameters according to any embodiment of the disclosure. As shown in fig. 7, the apparatus 700 specifically includes:
a training parameter list obtaining module 701, configured to obtain a training parameter list of a target data path obtained through link training, where the training parameter list includes multiple sets of link parameter values;
a training parameter list traversing module 702, configured to traverse the training parameter list and select a first target link parameter value from the training parameter list in response to the bit error rate on the target data path being higher than a preset threshold;
a link parameter configuration module 703, configured to configure the target data path according to the first target link parameter value.
Optionally, the apparatus further includes:
the bit error rate monitoring module is configured to monitor, according to a preset time interval, a bit error rate on the target data path after the link parameter configuration module 703 configures the target data path according to the first target link parameter value;
The link parameter configuration module is further configured to:
selecting a second target link parameter value from the training parameter list of the target data path in response to the error rate on the target data path again being higher than the preset threshold value and the training parameter list of the target data path not being traversed entirely;
and configuring the target data path according to the second target link parameter value.
Optionally, the link parameter configuration module is further configured to:
if the training parameter list of the target data path is completely traversed, acquiring the training parameter list of the adjacent data path of the target data path;
if the training parameter list of the adjacent data path is not completely traversed, selecting a third target link parameter value from the training parameter list of the adjacent data path;
and configuring the target data path according to the third target link parameter value.
Optionally, the link parameter configuration module is further configured to:
and if the training parameter list of the adjacent data path is completely traversed, carrying out link training on the target data path again.
Optionally, the training parameter list includes a plurality of quality factor values and link parameter values corresponding to the quality factor values, where the quality factor values are used to calibrate the quality of the data transmitted on the target data path, and each quality factor value is calculated according to the link parameter value corresponding to the quality factor value.
Optionally, the training parameter list traversing module 702 includes:
the traversing unit is used for traversing the training parameter list and determining a target quality factor value from the training parameter list;
and the first target link parameter value determining unit is used for taking the link parameter value corresponding to the target quality factor value in the training parameter list as the first target link parameter value.
Optionally, the traversing unit includes:
a current quality factor value determining subunit, configured to determine a current quality factor value corresponding to a current link parameter value configured on the target data path;
and the traversing subunit is used for traversing the training parameter list and taking the quality factor value smaller than the current quality factor value in the training parameter list as the target quality factor value.
Optionally, the traversing subunit is specifically configured to:
sorting the quality factor values in the training parameter list in descending order;
traversing the training parameter list according to the descending order, and taking the next quality factor value smaller than the current quality factor value in the training parameter list as the target quality factor value.
Optionally, the training parameter list is obtained through a specific control port provided by the receiving end of the target data path.
Optionally, the target data path is used for high-speed serial data transmission of the chip.
The product can execute the method provided by any embodiment of the disclosure, and has the corresponding functional modules and beneficial effects of executing the method.
In addition, the embodiment of the disclosure also provides a chip, which comprises a high-speed serial transmission system, wherein the high-speed serial transmission system comprises a plurality of data paths, each data path corresponds to a receiving end and a transmitting end, and the receiving end is used for carrying out link training on the corresponding data path; wherein,,
the high-speed serial transmission system further comprises a link parameter configuration module, which is specifically used for:
obtaining a training parameter list of a target data path obtained through link training through a receiving end of any target data path, wherein the training parameter list comprises a plurality of groups of link parameter values;
responding to the error rate on the target data path being higher than a preset threshold value, traversing the training parameter list, and selecting a first target link parameter value from the training parameter list;
And configuring the target data path according to the first target link parameter value.
The chip may be, for example, a voice chip, and is configured to receive a voice signal and perform processing such as voice recognition on the voice signal. The high-speed serial transmission system is a part of a chip and is used for transmitting high-speed serial signals with the outside. The link parameter configuration module is a part of the high-speed serial transmission system and is used for carrying out link parameter configuration on a data path in the high-speed serial transmission system so as to ensure the stability and the reliability of a high-speed transmission link. Meanwhile, the link parameter configuration module can realize the configuration method of the link parameters according to any embodiment of the disclosure, and has the corresponding functional modules and beneficial effects of executing the method.
In the technical scheme of the disclosure, the related processes of collecting, storing, using, processing, transmitting, providing, disclosing and the like of the personal information of the user accord with the regulations of related laws and regulations, and the public order colloquial is not violated.
According to embodiments of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium and a computer program product.
Fig. 8 illustrates a schematic block diagram of an example electronic device 800 that may be used to implement embodiments of the present disclosure. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the disclosure described and/or claimed herein.
As shown in fig. 8, the apparatus 800 includes a computing unit 801 that can perform various appropriate actions and processes according to a computer program stored in a Read Only Memory (ROM) 802 or a computer program loaded from a storage unit 808 into a Random Access Memory (RAM) 803. In the RAM 803, various programs and data required for the operation of the device 800 can also be stored. The computing unit 801, the ROM 802, and the RAM 803 are connected to each other by a bus 804. An input/output (I/O) interface 805 is also connected to the bus 804.
Various components in device 800 are connected to I/O interface 805, including: an input unit 806 such as a keyboard, mouse, etc.; an output unit 807 such as various types of displays, speakers, and the like; a storage unit 808, such as a magnetic disk, optical disk, etc.; and a communication unit 809, such as a network card, modem, wireless communication transceiver, or the like. The communication unit 809 allows the device 800 to exchange information/data with other devices via a computer network such as the internet and/or various telecommunication networks.
The computing unit 801 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of computing unit 801 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various computing units running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 801 performs the respective methods and processes described above, for example, a configuration method of link parameters. For example, in some embodiments, the method of configuring link parameters may be implemented as a computer software program tangibly embodied on a machine-readable medium, such as storage unit 808. In some embodiments, part or all of the computer program may be loaded and/or installed onto device 800 via ROM 802 and/or communication unit 809. When a computer program is loaded into RAM 803 and executed by computing unit 801, one or more steps of the configuration method of link parameters described above may be performed. Alternatively, in other embodiments, the computing unit 801 may be configured to perform the configuration method of the link parameters in any other suitable manner (e.g., by means of firmware).
Various implementations of the systems and techniques described here above can be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On Chip (SOCs), complex Programmable Logic Devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.
Program code for carrying out methods of the present disclosure may be written in any combination of one or more programming languages. These program code may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus such that the program code, when executed by the processor or controller, causes the functions/operations specified in the flowchart and/or block diagram to be implemented. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. The machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and pointing device (e.g., a mouse or trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), blockchain networks, and the internet.
The computer system may include a client and a server. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical hosts and VPS service are overcome. The server may also be a server of a distributed system or a server that incorporates a blockchain.
Artificial intelligence is the discipline of studying the process of making a computer mimic certain mental processes and intelligent behaviors (e.g., learning, reasoning, thinking, planning, etc.) of a person, both hardware-level and software-level techniques. Artificial intelligence hardware technologies generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing, and the like; the artificial intelligent software technology mainly comprises a computer vision technology, a voice recognition technology, a natural language processing technology, a machine learning/deep learning technology, a big data processing technology, a knowledge graph technology and the like.
Cloud computing (cloud computing) refers to a technical system that a shared physical or virtual resource pool which is elastically extensible is accessed through a network, resources can comprise servers, operating systems, networks, software, applications, storage devices and the like, and resources can be deployed and managed in an on-demand and self-service mode. Through cloud computing technology, high-efficiency and powerful data processing capability can be provided for technical application such as artificial intelligence and blockchain, and model training.
It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps recited in the present disclosure may be performed in parallel, sequentially, or in a different order, provided that the desired results of the technical solutions provided by the present disclosure are achieved, and are not limited herein.
The above detailed description should not be taken as limiting the scope of the present disclosure. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present disclosure are intended to be included within the scope of the present disclosure.
Claims (13)
1. A method of configuring link parameters, comprising:
obtaining a training parameter list of a target data path obtained through link training, wherein the training parameter list comprises a plurality of groups of link parameter values, and the link parameter values of different groups are combinations of different values of each link parameter;
traversing the training parameter list in response to the bit error rate on the target data path being higher than a preset threshold, and selecting another set of link parameter values different from the currently used link parameter values from the training parameter list as first target link parameter values;
configuring the target data path according to the first target link parameter value;
after said configuring said target data path according to said first target link parameter value, said method further comprises:
Monitoring the error rate on the target data path according to a preset time interval;
selecting a second target link parameter value from the training parameter list of the target data path in response to the error rate on the target data path again being higher than the preset threshold value and the training parameter list of the target data path not being traversed entirely;
and configuring the target data path according to the second target link parameter value.
2. The method of claim 1, wherein after said configuring said target data path according to said first target link parameter value, said method further comprises:
if the training parameter list of the target data path is completely traversed, acquiring the training parameter list of the adjacent data path of the target data path;
if the training parameter list of the adjacent data path is not completely traversed, selecting a third target link parameter value from the training parameter list of the adjacent data path;
and configuring the target data path according to the third target link parameter value.
3. The method of claim 2, wherein after said configuring said target data path according to said first target link parameter value, said method further comprises:
And if the training parameter list of the adjacent data path is completely traversed, carrying out link training on the target data path again.
4. The method of claim 1, wherein the training parameter list includes a plurality of quality factor values and link parameter values corresponding to the quality factor values, the quality factor values being used to scale the quality of the data transmitted on the target data path, and each quality factor value being calculated from the link parameter values corresponding thereto.
5. The method of claim 4, wherein the traversing the training parameter list and selecting a first target link parameter value from the training parameter list comprises:
traversing the training parameter list, and determining a target quality factor value from the training parameter list;
and taking a link parameter value corresponding to the target quality factor value in the training parameter list as the first target link parameter value.
6. The method of claim 5, wherein the traversing the training parameter list and determining a target quality factor value from the training parameter list comprises:
determining a current quality factor value corresponding to a current link parameter value configured on the target data path;
Traversing the training parameter list, and taking the quality factor value smaller than the current quality factor value in the training parameter list as the target quality factor value.
7. The method of claim 6, wherein the traversing the training parameter list and taking as the target figure of merit value figure of merit values in the training parameter list that are less than the current figure of merit value comprises:
sorting the quality factor values in the training parameter list in descending order;
traversing the training parameter list according to the descending order, and taking the next quality factor value smaller than the current quality factor value in the training parameter list as the target quality factor value.
8. The method of claim 1, wherein the training parameter list is obtained through a particular control port provided by a receiving end of the target data path.
9. The method of claim 1, wherein the target data path is for high-speed serial data transmission by a chip.
10. A configuration apparatus of link parameters, comprising:
the system comprises a training parameter list acquisition module, a link parameter selection module and a link parameter selection module, wherein the training parameter list is used for acquiring a training parameter list of a target data path obtained through link training, the training parameter list comprises a plurality of groups of link parameter values, and the link parameter values of different groups are combinations of different values of each link parameter;
The training parameter list traversing module is used for traversing the training parameter list and selecting another group of link parameter values which are different from the currently used link parameter values from the training parameter list as first target link parameter values in response to the error rate on the target data channel being higher than a preset threshold;
a link parameter configuration module, configured to configure the target data path according to the first target link parameter value;
the error rate monitoring module is used for monitoring the error rate on the target data path according to a preset time interval after the link parameter configuration module configures the target data path according to the first target link parameter value;
the link parameter configuration module is further configured to:
selecting a second target link parameter value from the training parameter list of the target data path in response to the error rate on the target data path again being higher than the preset threshold value and the training parameter list of the target data path not being traversed entirely;
and configuring the target data path according to the second target link parameter value.
11. An electronic device, comprising:
At least one processor; and
a memory communicatively coupled to the at least one processor; wherein,,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of configuring link parameters of any one of claims 1-9.
12. A non-transitory computer-readable storage medium storing computer instructions for causing a computer to perform the method of configuring link parameters according to any one of claims 1-9.
13. The chip comprises a high-speed serial transmission system, wherein the high-speed serial transmission system comprises a plurality of data paths, each data path corresponds to a receiving end and a transmitting end, and the receiving end is used for carrying out link training on the data path corresponding to the receiving end; wherein,,
the high-speed serial transmission system further comprises a link parameter configuration module, which is specifically used for:
obtaining a training parameter list of a target data path obtained through link training through a receiving end of any target data path, wherein the training parameter list comprises a plurality of groups of link parameter values, and the link parameter values of different groups are combinations of different values of each link parameter;
Traversing the training parameter list in response to the bit error rate on the target data path being higher than a preset threshold, and selecting another set of link parameter values different from the currently used link parameter values from the training parameter list as first target link parameter values;
configuring the target data path according to the first target link parameter value;
the high-speed serial transmission system further comprises an error rate monitoring module, which is used for monitoring the error rate on the target data path according to a preset time interval after the link parameter configuration module configures the target data path according to the first target link parameter value;
the link parameter configuration module is further configured to:
selecting a second target link parameter value from the training parameter list of the target data path in response to the error rate on the target data path again being higher than the preset threshold value and the training parameter list of the target data path not being traversed entirely;
and configuring the target data path according to the second target link parameter value.
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