A distributed-based FPGA partial dynamic reconfiguration componentization method

Abstract

The application relates to an intelligent reconstruction method based on FPGA distributed components, first, historical data are collected, arranged into a data set, input into a system state prediction model for training, and a trained model is obtained; after the system is initialized, each node is monitored in real time, and monitoring data are sent into the trained system state prediction model for prediction to obtain the state of the future system; if the future system state is abnormal, an FPGA dynamic reconstruction decision is made and executed to ensure the stability of the system; if there is no abnormality, the system state monitoring is continuously carried out; when a certain condition is triggered, the model is iteratively updated according to the data collected in the recent period, so that the long-term stability of the system is ensured. The application combines deep learning technology, carries out real-time state monitoring and prediction in the system running process, and can accurately predict problems such as future possible blocking, resource conflict or performance decline.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of artificial intelligence, and proposes a distributed FPGA partial dynamic reconfiguration component method. The method is based on FPGA distributed components, combines deep learning models for system state monitoring and prediction, accurately identifies potential performance bottlenecks and congestion trends, and adjusts and optimizes FPGA functional components in real time through dynamic partial reconfiguration technology, ensuring the stability and efficiency of the system under high load and long time operation. BACKGROUND

[0002] With the increasing demand for complex computing tasks and large-scale data processing, traditional FPGA architecture gradually shows limitations in long-time high-load running environment. Traditional methods have significant bottlenecks in resource allocation, task scheduling and system running efficiency, often leading to component flow congestion, uneven resource allocation, performance degradation and other problems, which seriously affect the overall running efficiency of the system, and even cause task interruption or failure. This static configuration architecture cannot flexibly adapt to dynamic changes in computing demand, and is difficult to maintain stability and efficiency in high-intensity and long-time running scenarios. Therefore, how to realize the efficient dynamic management and resource optimization of FPGA architecture has become an important technical problem to be solved.

[0003] In recent years, deep learning technology has made significant breakthroughs, especially in data prediction, pattern recognition and adaptive optimization. Through training a large amount of historical data, deep learning models can accurately predict system state, identify potential problems and make optimization decisions in advance. Its adaptive learning and iterative optimization capabilities enable the model to continuously improve performance in complex and dynamic environments. These characteristics make deep learning show great potential in system state monitoring, resource allocation optimization and fault prediction, providing new ideas and methods to solve the technical bottlenecks faced by traditional FPGA architecture.

[0004] The combination of deep learning and FPGA dynamic partial reconfiguration technology has become a key research direction in the academic and industrial communities. By using deep learning models to monitor and intelligently evaluate the state of FPGA systems in real-time, potential blockages and resource conflicts during system operation can be accurately predicted, enabling dynamic resource allocation and adaptive adjustment of functional modules. Based on this combination, the system can dynamically reconfigure the FPGA without affecting the overall task operation, flexibly addressing resource bottlenecks and performance issues in high-intensity, long-duration scenarios. In addition, the introduction of a distributed architecture further enhances the collaborative efficiency and resource sharing capabilities of multiple FPGA nodes, providing strong support for large-scale data processing and high-performance computing scenarios. This deep integration not only provides more efficient computing performance, improves real-time performance and flexibility, but also effectively addresses issues such as high power consumption, poor adaptability, and waste of hardware resources in existing technologies. This combination has important advantages in practical applications, especially in embedded systems and edge computing scenarios that require high performance, low power consumption, and flexible configuration, significantly improving the overall performance and scalability of the system. SUMMARY

[0005] The present application aims to overcome the shortcomings of the prior art and provide a distributed FPGA partial dynamic reconfiguration componentization method that combines deep learning technology to perform real-time state monitoring and prediction during system operation, accurately predicting potential blockages, resource conflicts, or performance degradation in the future.

[0006] The purpose of the present application is achieved by the following technical solution: a distributed FPGA partial dynamic reconfiguration componentization method, comprising the following steps:

[0007] Step one, monitor and collect data from multiple FPGA servers, including CPU usage, memory usage, and hard disk usage;

[0008] Step two, preprocess the historical monitoring data: first, normalize the CPU usage, memory usage, and hard disk usage data to zero mean; then standardize and crop the data into multiple samples of length m; and generate sample labels for each sample: if the data within the next two hours does not show full load, set the sample label to 0, otherwise set it to 1;

[0009] The normalization expression is as follows:

[0010]

[0011] Where X represents the original data, X *represents normalized data, mu represents the mean of the original data, and delta represents the standard deviation of the original data; the specific expressions of mu and delta are as follows:

[0012]

[0013] n is the sample number, x i is the i-th sample in the original data;

[0014] Step three, training the deep learning network model by using the data obtained in step two;

[0015] Step four, system initialization of the distributed FPGA service cluster: firstly, self-checking and verification are performed on the FPGA hardware resources to ensure that the logic unit, storage unit, clock management module and communication interface are normally operated; then, the system loads the basic configuration file, initializes the dynamic reconfiguration controller, presets the reconfiguration area and the partition of the reconfigurable logic module, and establishes the dynamic reconfiguration management table;

[0016] Step five, starting the monitoring module of the system to complete the real-time monitoring of the state of the cluster system, and collecting the CPU usage, memory usage and hard disk usage of each server node of the cluster in real time;

[0017] Step six, after the real-time collected data is normalized and standardized and clipped, the data is input into the deep learning network model trained in step three for prediction, to obtain the future system state result and perform abnormal state discrimination; when the system does not have an abnormality, the real-time monitoring of step five is returned; when the system has an abnormality, the abnormal server node is found, and a dynamic reconfiguration scheme is generated;

[0018] Step seven, executing the FPGA dynamic reconfiguration scheme, using the FPGA dynamic partial reconfiguration technology, loading a new Bitstream to the specified area or pausing the FPGA component service, and self-checking the reconfigured FPGA server to ensure that each component is normally operated.

[0019] The beneficial effects of the present application are:

[0020] (1) The present application can perform intelligent prediction and adaptive optimization;

[0021] The application combines deep learning technology to perform real-time state monitoring and prediction during system operation, which can accurately predict potential future problems such as congestion, resource conflicts or performance degradation. This prediction mechanism relies on training through historical data and input of real-time monitoring data to build a high-precision system state prediction model. Unlike traditional passive fault repair mode, this method can actively develop optimization decisions before potential problems appear, reducing the probability of failure and avoiding the impact of performance bottlenecks on overall system efficiency. At the same time, the system also has adaptive optimization capability, dynamically adjusting system parameters according to different task scenarios and operating environments to ensure optimal resource utilization. This intelligent adaptive mechanism not only reduces the need for human intervention, but also improves system stability and response speed, maintaining sustained high-performance output in complex and variable application environments.

[0022] (2) The application has efficient dynamic reconfiguration capability;

[0023] The application realizes dynamic replacement, reconfiguration and resource adjustment of specific function modules during system operation by introducing FPGA dynamic partial reconfiguration technology. This reconfiguration process can be performed without interrupting the overall operation of the system, ensuring the continuity and stability of the task. In a long-time high-load running environment, traditional FPGA architecture often causes performance bottlenecks and even system crashes due to resource solidification and lack of flexibility. When potential problems such as system performance degradation, resource conflicts or component flow congestion are detected, the invention can quickly trigger a dynamic reconfiguration process to redistribute computing resources or replace faulty components to restore normal system operation. In addition, this dynamic reconfiguration not only improves the flexibility of the system, but also avoids task failure caused by single-point failure, improving the reliability and stability of the system in complex computing task scenarios. Therefore, this method has significant advantages in the face of high real-time, strong adaptability and long-time continuous operation application requirements.

[0024] (3) The application supports distributed architecture collaborative work.

[0025] The distributed architecture design is adopted, and efficient cooperation and resource sharing between multiple FPGA nodes are realized. This architecture enables multiple FPGA devices to cooperate with each other in large-scale computing tasks, forming an efficient and flexible computing network. Under the distributed architecture, each node can independently complete a specific computing task, while sharing resources when needed, avoiding the situation of system crash caused by resource exhaustion of a single node. In addition, the distributed cooperative working mechanism makes the system show stronger flexibility and scalability in scenarios such as high-concurrency tasks, large data processing and cross-node resource scheduling. Even in the case of node failure, the system can still guarantee the continuity and stability of the task through dynamic resource scheduling and task transfer. The introduction of this distributed architecture provides strong support for complex computing tasks and can significantly improve the overall efficiency and stability of the system.

[0026] (4) The present application supports continuous model iteration and optimization.

[0027] The system state prediction model of the present application has the ability of self-iteration and continuous optimization, and can continuously train and update the model according to the recently collected system monitoring data. This mechanism enables the system to maintain high prediction accuracy and adaptability during long-term operation. When the system triggers a specific condition, the model will automatically enter the iterative update process, adjusting the model parameters with the latest monitoring data to better match the current system state and task requirements. This continuous optimization process not only improves the adaptability of the system in complex environments, but also avoids the problem of model aging and prediction inaccuracy. In addition, through the automatic model training and deployment process, the system reduces manual intervention and operation and maintenance costs, ensuring efficient prediction and decision-making ability in different scenarios. This continuous iteration and optimization mechanism provides a strong guarantee for the stable operation of the system in long-term high-load scenarios. BRIEF DESCRIPTION OF DRAWINGS

[0028] Figure 1 The present application is based on the distributed FPGA part dynamic reconfiguration component method flowchart;

[0029] Figure 2 The present application is based on the distributed FPGA cluster hardware composition schematic diagram;

[0030] Figure 3 The present application is based on the distributed FPGA part dynamic reconfiguration component method system service composition schematic diagram;

[0031] Figure 4 The system state prediction network designed by the present application is shown in the schematic diagram;

[0032] Figure 5A system state prediction network structure diagram for implementing the present application. DETAILED DESCRIPTION

[0033] The present application provides a distributed FPGA partial dynamic reconfiguration component method, which is based on FPGA distributed components, combines deep learning models for system state monitoring and prediction, accurately identifies potential performance bottlenecks and congestion trends, and adjusts and optimizes FPGA functional components in real time through dynamic partial reconfiguration technology, ensuring the stability and efficiency of the system under high load and long time operation.

[0034] The technical solutions of the present application will be further described below with reference to the accompanying drawings.

[0035] As shown in Figure 1 A distributed FPGA partial dynamic reconfiguration component method according to the present application includes the following steps:

[0036] Step 1: Monitor and collect data of the running state of multiple FPGA servers, collect historical monitoring data of the server, including CPU usage, memory usage, and hard disk usage; the hardware composition diagram of the distributed FPGA cluster is as shown in Figure 2 The system is composed of a main server and multiple FPGA servers through a core switch, the main server is equipped with a GPU computing graphics card, the FPGA server is equipped with multiple FPGA computing board cards, data is accessed to the cluster system through the core switch, the cluster sends data to each FPGA server for calculation and inference according to the data business, and the calculation results are returned.

[0037] Step 2: Preprocess the historical monitoring data: first, normalize the CPU usage, memory usage, and hard disk usage data to zero mean; then standardize and crop the data to multiple samples with length m; and generate sample labels for each sample: if the data of a sample within the next two hours does not appear full load condition, it means no load exception, and the label of the sample is set to 0, otherwise it is set to 1; the constructed data set contains N samples, the dimension of the data set is N*3*m, and the samples are divided into training set and validation set;

[0038] The normalized expression is as follows:

[0039]

[0040] Where X represents the original data, X * represents the normalized data, mu represents the mean of the original data, and delta represents the standard deviation of the original data; the specific expressions of mu and delta are as follows:

[0041]

[0042] n is the number of samples, x i is the i-th sample in the original data;

[0043] Step three, use the training set and validation set data obtained in step two to train the deep learning network model. The training set and validation set are sent into the model training together; the training set is used to train the model; the validation set is used to evaluate the training effect of the model during the training process, and does not participate in the back propagation. The model predicts the input data to obtain the system state. The error estimation is constructed for the predicted data and the real data, and the model is updated by the optimizer through back propagation to update the model parameters. After multiple iterations of training, the model area is stable, the prediction data and the real data error become very small, the validation set has high accuracy, the model can stop training, and the trained deep learning model is obtained. The trained model can accurately predict the future running state of the system.

[0044] Step four, system initialization of distributed FPGA service cluster: first, self-check and verify the FPGA hardware resources to ensure that the logic unit, storage unit, clock management module and communication interface are running normally; then, the system loads the basic configuration file, initializes the dynamic reconfiguration controller, presets the reconfiguration area and the partition of the reconfigurable logic module, and establishes the dynamic reconfiguration management table;

[0045] Step five, the system starts the monitoring module to complete the real-time monitoring of the cluster system state, and collects the CPU usage, memory usage and hard disk usage of each server node in the cluster in real time to provide data support for subsequent dynamic reconfiguration decision;

[0046] Step six, after the real-time collected data is preprocessed such as normalization and standardization clipping, it is input into the deep learning network model trained in step three for prediction to obtain the future system state result and perform abnormal state discrimination; when the system does not exist, return to step five for real-time monitoring; when the system exists, find the abnormal server node and generate a dynamic reconfiguration scheme;

[0047] Step seven, execute the FPGA dynamic reconfiguration scheme, use the FPGA dynamic partial reconfiguration technology, load the new Bitstream to the specified area or pause the FPGA component service, and self-check the reconfigured FPGA server to ensure that each component is running normally.

[0048] Step 8: Return to step 5 for real-time monitoring and organize the abnormal data. The present invention can also set up timed training. When the conditions are met, the system will organize the historical data of the recent period and go to step 2 to update the historical data, retraining the model to continuously iterate and optimize the model for incremental learning, thereby ensuring the long-term stability of the system.

[0049] The present invention is based on the distributed FPGA partial dynamic reconstruction component method system service composition diagram as shown in the following figure: Figure 3 As shown in the figure, the system main server is deployed with training service, prediction service, deployment service, and monitoring service, and the computing service is deployed in the FPGA server; the training service is used to complete the iterative update of the system status prediction model, the prediction service is used to complete the reasoning of the future system status, the monitoring service is used to complete the status monitoring of the system and each sub-computing node, and the deployment service is used to formulate dynamic reconstruction plans and issue instructions.

[0050] The system state prediction network designed by the present invention has a three-input and single-output network structure, such as Figure 4 The principle of the network structure is to extract features from different input data separately through the convolution module, splice the extracted features using the feature splicing module, continue to use the convolution module to extract high-order features from the spliced ​​features, and then use the fully connected module to integrate the features and map them to the single output feature space. The specific network structure of this embodiment is as follows Figure 5 As shown, the input size of each input layer is 1*8192*1, and each input layer is followed by a CNN network feature extraction module composed of network convolution, standardization and pooling; the CNN network feature extraction module first uses the convolution kernel to extract features from the input feature matrix, superimposes the extraction results and the input data on the matrix and uses standardization processing, and finally performs pooling processing; the data is sequentially processed using 512, 256, and 128 convolution kernel modules for feature processing on the three inputs, and 1*2 pooling is performed to obtain three feature matrices of size 1*1024*128; the outputs of the three CNN network feature extraction modules are then spliced ​​through the network feature connection module according to the feature matrix splicing method to obtain a feature matrix of 1*1024*384; continue to input the CNN network feature extraction module composed of 64, 128, 256, and 512 convolution kernel modules for feature processing, and obtain a 1*64*512 feature vector. After flattening, a 512-dimensional fully connected module is used for feature convergence, and finally output through a 1-dimensional full connection.

[0051] Those skilled in the art will appreciate that the embodiments described herein are presented for purposes of illustration and that the inventive principles are not limited to these particular embodiments. Other variations and modifications can be made to the embodiments without departing from the spirit and scope of the inventive principles.

Claims

1. A distributed FPGA partial dynamic reconfiguration componentization method, characterized in that: The following steps are involved: Step 1: Monitor and collect data on the operating status of multiple FPGA servers, and collect historical monitoring data of the servers, including three historical load status data: CPU usage, memory usage, and hard disk usage; Step 2: Preprocess the historical monitoring data: First, perform zero-mean normalization on the CPU usage, memory usage, and hard disk usage data; then standardize and crop the data into multiple samples of length m; And generate a sample label for each sample: if the data of a sample does not show full load in the next two hours, the label of the sample is set to 0, otherwise it is set to 1; The normalization expression is as follows: Among them, X represents the original data, X * represents the normalized data, μ represents the mean of the original data, and δ represents the standard deviation of the original data; the specific expressions of μ and δ are as follows: n is the number of samples, x i is the i-th sample in the original data; Step 3: Use the data obtained in step 2 to train the deep learning network model; the network structure is a three-input single-output network structure, the input size of each input layer is 1*8192*1, and each input layer is followed by a CNN network feature extraction module consisting of network convolution, standardization and pooling; the CNN network feature extraction module first uses the convolution kernel to extract features from the input feature matrix, superimposes the extraction result and the input data on a matrix, and uses standardization processing, and finally performs pooling processing; the data is sequentially subjected to 512, 256, and 128 convolution kernel modules for the three inputs. Processing and 1*2 pooling are performed to obtain three feature matrices of size 1*1024*128; the outputs of the three CNN network feature extraction modules are then spliced ​​through the network feature connection module according to the feature matrix splicing method to obtain a 1*1024*384 feature matrix; continue to input the CNN network feature extraction module composed of 64, 128, 256, and 512 convolution kernel modules for feature processing, and obtain a 1*64*512 feature vector. After flattening, a 512-dimensional fully connected module is used for feature convergence, and finally output through a 1-dimensional full connection; Step 4: Initialize the distributed FPGA service cluster: First, perform self-test and verification on the FPGA hardware resources to ensure the normal operation of the logic unit, storage unit, clock management module, and communication interface. Next, the system loads the basic configuration file, initializes the dynamic reconfiguration controller, presets the partitions of the reconfigurable area and reconfigurable logic module, and establishes the dynamic reconfiguration management table. Step 5: The system starts the monitoring module to complete the real-time monitoring of the cluster system status and collect the CPU usage, memory usage, and hard disk usage of each server node in the cluster in real time; Step 6: After normalizing and standardizing the data collected in real time, input it into the deep learning network model trained in step 3 for prediction, obtain the future system status results and perform abnormal state identification; if the system does not have an abnormality, return to step 5 for real-time monitoring; if the system does have an abnormality, find the abnormal server node and generate a dynamic reconstruction plan; Step 7: Execute the FPGA dynamic reconstruction solution. Use the FPGA dynamic partial reconstruction technology to load a new bitstream to the specified area or suspend the FPGA component service. Perform a self-test on the reconstructed FPGA server to ensure that all components are operating normally.

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