Adaptive gridding FPGA waveform pipeline dynamic reconstruction method and system

Abstract

The invention provides a self-adaptive gridding FPGA (Field Programmable Gate Array) waveform assembly line dynamic reconstruction method and system, which introduces a self-adaptive gridding algorithm, depends on modeling and safe sequence planning based on DAG (Directed Acyclic Graph), and an assembly line parallel scheduling and integrated time sequence calibration mechanism. The problems that in an existing FPGA waveform dynamic reconstruction technology, the resource fragmentation problem is caused by static partition, the system reliability risk is caused by component dependence management deficiency, and the reconstruction delay is large and the real-time performance is poor due to a serial reconstruction mode are solved. According to the method, the utilization rate of FPGA logic resources is remarkably increased, the safety and controllability of the reconstruction process are achieved, the overall reconstruction time is greatly shortened, and meanwhile the processing precision after reconstruction is guaranteed through self-adaptive time sequence calibration. And an efficient hardware dynamic reconstruction solution is provided for application scenes with extremely high requirements on real-time performance and reliability, such as software defined radio and integrated radio frequency systems.

Description

Technical Field

[0001] This invention relates to the field of digital signal processing technology, and in particular to an adaptive meshing FPGA waveform pipeline dynamic reconstruction method and system. Background Technology

[0002] As communication equipment evolves towards standardization and integration, individual devices need to support multiple waveform protocols. Due to limitations in the manufacturing process of domestically produced FPGA chips, their logic resources are relatively precious, making it difficult to meet all requirements through static deployment. Dynamic reconfiguration technology has become crucial. However, in high real-time scenarios such as communication, achieving reliable and efficient dynamic reconfiguration to avoid system failures and performance degradation remains a major technical challenge.

[0003] Existing FPGA dynamic reconfiguration technologies primarily employ a combination of static partitioning and pre-compiled bitstreams. During the design phase, the FPGA resources are divided into fixed static areas and several dynamically reconfigurable regions (PRRs) of predefined size and shape. At runtime, through internal configuration access ports, pre-generated portions of the bitstream are loaded onto designated PRRs as needed to achieve hardware function switching. However, in complex communication application scenarios, existing technologies still suffer from the following drawbacks:

[0004] 1) Rigid reconfiguration area partitioning: Existing technologies use fixed PRR boundaries for static partitioning, which cannot adapt to the dynamic resource requirements of different waveform components, resulting in low resource utilization and even reconfiguration failure; 2) Complex state management and dependencies: Communication and state synchronization between components during reconfiguration mainly rely on manual configuration, which may lead to data path errors or system deadlock, increasing the risk of system failure and affecting reliability; 3) High delay and overhead in the reconfiguration process: The reconfiguration process mostly adopts a serial execution mode, with the total delay being the sum of the reconfiguration times of each circuit, resulting in low efficiency; moreover, the reconfiguration process may change the wiring, causing clock skew and affecting the performance of high-speed interfaces.

[0005] Therefore, how to research and design an adaptive meshing FPGA waveform pipeline dynamic reconstruction method and system that can overcome the above-mentioned defects is a problem that we urgently need to solve. Summary of the Invention

[0006] To address the shortcomings of existing technologies, this invention provides an adaptive meshing FPGA waveform pipeline dynamic reconfiguration method and system. It dynamically allocates suitable continuous physical regions to waveform components using an adaptive meshing algorithm; generates a safe reconfiguration sequence by constructing a directed acyclic graph model of component dependencies and analyzing critical paths, avoiding state conflicts; and finally employs a pipelined parallel scheduling strategy to execute the reconfiguration task, integrating real-time timing calibration and state monitoring rollback mechanisms to ensure rapid system recovery in case of anomalies. This invention solves the problems of low resource utilization, high failure rate, and low serial efficiency in existing technologies, achieving efficient and reliable adaptive dynamic reconfiguration.

[0007] According to an embodiment of the present invention, an adaptive meshing FPGA waveform pipeline dynamic reconstruction method includes:

[0008] S1: Based on the resource requirements of the waveform components to be deployed and the real-time distribution status of FPGA resources, dynamically reconstruct the region and generate a grid layout scheme.

[0009] S2: Construct a DAG model based on the data flow and control flow dependencies between waveform components, and perform topology sorting and critical path analysis based on the DAG model to obtain a safe reconstruction sequence;

[0010] S3: Based on the security reconstruction sequence and the grid layout scheme, a pipelined parallel scheduling strategy is adopted to arrange the execution order of reconstruction tasks, and data communication and timing calibration are performed simultaneously to generate a reconstruction execution plan;

[0011] S4: According to the reconfiguration execution plan, load a portion of the reconfiguration bitstream into the corresponding dynamic reconfiguration area through the FPGA configuration interface, while monitoring the component state migration, and initiating a rollback mechanism when an anomaly occurs, until all components are successfully deployed.

[0012] Furthermore, the dynamic reconstruction region division adopts an adaptive grid division algorithm, which dynamically adjusts the size and shape of the grid cells according to the real-time resource requirements of each component, and maps each component to an independent grid cell;

[0013] The minimum size of the grid cell is 2×2 LUT granularity, and the maximum size is 8×8 LUT granularity;

[0014] If there are components with close data interaction, prioritize allocating the reconstructed regions that are adjacent or have the shortest communication paths.

[0015] Furthermore, the adaptive mesh partitioning algorithm also includes a greedy merging strategy:

[0016] Based on the current resource requirements of the waveform components, all unoccupied and physically adjacent free grid cells are traversed in the FPGA resource layout.

[0017] Calculate the combined area of ​​these adjacent free grid cells and determine whether it is greater than or equal to the minimum resource area threshold required by the current waveform component;

[0018] If the combined area meets the component resource area requirements, these free grid cells are merged into a continuous rectangular region and allocated to the current waveform component for use, and the occupancy status of this region is updated in the grid layout scheme.

[0019] Furthermore, the DAG model is constructed using waveform components as nodes and the data flow between components as directed edges. The edge weights of the DAG model are dynamically updated based on real-time running data, which includes one or more of the following: input rate, output rate, processing latency, logic resource utilization, and error rate.

[0020] Furthermore, the topological sorting is implemented based on the data dependency constraints between nodes in the DAG model. The sequence of nodes with an in-degree of 0 is identified by the breadth-first search algorithm, and a basic reconstruction order that satisfies the predecessor-successor relationship between components is generated.

[0021] The critical path analysis is achieved by summing the node reconstruction time and the edge communication overhead. The longest path in the DAG is calculated using a dynamic programming algorithm to determine the sequence of critical components with the largest total time consumption during the reconstruction process. The overall reconstruction time is optimized by prioritizing the scheduling of components on the critical path.

[0022] Furthermore, the pipelined parallel scheduling strategy divides the reconfiguration process into multiple independent stages and allocates the reconfiguration tasks of different grid units to the independent stages for parallel execution.

[0023] The timing calibration is based on the phase adjustment function of the FPGA's internal clock management unit to fine-tune the relative timing of the configuration data loading, or to achieve timing synchronization between multiple modules by inserting a programmable number of synchronization wait cycles.

[0024] Furthermore, the timing calibration is achieved through a least mean square adaptive filtering algorithm, dynamically adjusting parameters based on the real-time monitored timing error. The calibration formula is as follows:

[0025] ;

[0026] in, It is the first The estimated time series error for the next iteration This is the step size factor, which controls the convergence speed and stability. It is the mean square error performance function about The gradient.

[0027] Furthermore, the state transition of the monitoring component includes:

[0028] Before reconstruction, the register values ​​and memory state of the dynamic region are saved to a dedicated backup area through a state snapshot mechanism;

[0029] After refactoring, monitor the validity of the ready signals and output data of the monitoring components.

[0030] Furthermore, after the rollback mechanism is triggered, the FPGA configuration interface controls the configuration of the abnormal dynamic region to roll back to the previous known stable version, and uses the state snapshot to restore the operating state of the components in that region.

[0031] The anomalies include configuration frame verification errors, component initialization timeouts, or abnormal function outputs.

[0032] On the other hand, according to embodiments of the present invention, an adaptive meshing FPGA waveform pipeline dynamic reconstruction system is also provided, the system being used to implement an adaptive meshing FPGA waveform pipeline dynamic reconstruction method as described in any one of the first aspects, comprising:

[0033] The resource partitioning unit includes a resource status monitoring module and an adaptive mesh generation module, which are used to monitor the real-time distribution status of FPGA logic resources and run an adaptive mesh partitioning algorithm to generate a dynamic mesh layout scheme based on the resource requirements of the waveform components to be deployed.

[0034] The path planning unit, including the DAG modeling module and the critical path analysis module, is used to construct a DAG model based on the data flow and control flow dependencies between waveform components, and to perform topology sorting and critical path analysis to generate a safe reconfiguration sequence.

[0035] The scheduling and execution unit includes a pipeline scheduler, a timing calibration module, and a status monitoring module, wherein:

[0036] The pipeline scheduler is used to orchestrate pipelined strategies and execute reconstruction tasks based on the security reconstruction sequence and the grid layout scheme.

[0037] The timing calibration module is used to perform data communication and timing calibration simultaneously during the reconstruction process;

[0038] The status monitoring module is used to monitor component status transitions when loading a portion of the reconfiguration bitstream through the FPGA configuration interface, and to initiate a rollback mechanism in case of an anomaly.

[0039] The configuration management unit includes a bitstream library management module and a version control module, which are used to store and manage multiple versions of partially reconfigured bitstream files, and provide the correct configuration files for the corresponding dynamic reconfiguration areas according to the reconfiguration execution plan.

[0040] The technical principle of this invention is as follows: An adaptive intelligent management mechanism optimizes the entire process of FPGA dynamic reconfiguration. First, based on the real-time resource requirements of the waveform components, an adaptive mesh partitioning algorithm dynamically allocates the reconfiguration region, improving resource utilization and avoiding fragmentation. Second, by constructing a directed acyclic graph model of inter-component dependencies, critical path analysis is performed to automatically generate a safe and reliable reconfiguration sequence, ensuring correct system state transition. Finally, during the execution phase, a pipelined parallel scheduling strategy is adopted to shorten the reconfiguration latency, and real-time status monitoring and automatic rollback mechanisms are integrated, combined with timing calibration technology, to ensure the efficiency and reliability of the reconfiguration process, forming a closed loop from intelligent planning to safe execution.

[0041] Compared with the prior art, the present invention has the following beneficial effects:

[0042] 1. This invention uses an adaptive mesh partitioning algorithm to dynamically divide FPGA resources into fine-grained mesh cells and employs a greedy merging strategy to allocate suitable continuous regions to waveform components. This solves the problem of resource waste caused by static partitioning being unable to adapt to the dynamic resource requirements of components, significantly improving the utilization rate of FPGA logic resources and effectively avoiding reconstruction failures caused by a lack of sufficiently large continuous free regions.

[0043] 2. This invention automatically generates a safe reconfiguration sequence by constructing a directed acyclic graph (DAG) model of component dependencies and performing topology sorting and critical path analysis. This solves the risk of system state conflicts or deadlocks that may be caused by the traditional serial loading order ignoring the data / control flow dependencies between components. At the same time, by prioritizing the scheduling of components on the critical path, the overall reconfiguration time is optimized, ensuring the efficiency and reliability of the reconfiguration process.

[0044] 3. This invention adopts a pipelined parallel scheduling strategy, decomposes the reconstruction task into multiple stages for parallel execution, and combines the least mean square adaptive filtering algorithm to calibrate the timing deviation that may be introduced by reconstruction in real time. It overcomes the shortcomings of long delay and low efficiency of traditional serial reconstruction methods, and compensates for the clock offset that may be caused by changes in wiring due to reconstruction, thereby greatly shortening the reconstruction time and ensuring the performance stability and accuracy of the high-speed signal processing link. Attached Figure Description

[0045] Figure 1 This is a flowchart illustrating the steps of an embodiment of the present invention.

[0046] Figure 2This is a schematic diagram of the overall scheme of an embodiment of the present invention.

[0047] Figure 3 This is a schematic diagram of adaptive mesh partitioning according to an embodiment of the present invention.

[0048] Figure 4 This is a flowchart of the greedy merging algorithm according to an embodiment of the present invention.

[0049] Figure 5 This is a DAG modeling and critical path analysis diagram for an embodiment of the present invention.

[0050] Figure 6 This is a pipeline scheduling diagram according to an embodiment of the present invention.

[0051] Figure 7 This is a diagram illustrating the intelligent engine monitoring and management system according to an embodiment of the present invention.

[0052] Figure 8 This is a system block diagram of another embodiment of the present invention. Detailed Implementation

[0053] The technical solutions of the present invention will be further described below with reference to the accompanying drawings and embodiments.

[0054] like Figure 1 As shown in the figure, this invention proposes an adaptive meshing FPGA waveform pipeline dynamic reconstruction method, including:

[0055] S1: Based on the resource requirements of the waveform components to be deployed and the real-time distribution status of FPGA resources, dynamically reconstruct the region and generate a grid layout scheme.

[0056] S2: Construct a DAG model based on the data flow and control flow dependencies between waveform components, and perform topology sorting and critical path analysis based on the DAG model to obtain a safe reconfiguration sequence;

[0057] S3: Based on the security reconfiguration sequence and grid layout scheme, a pipelined parallel scheduling strategy is adopted to arrange the execution order of reconfiguration tasks, and data communication and timing calibration are performed simultaneously to generate a reconfiguration execution plan;

[0058] S4: According to the reconfiguration execution plan, load part of the reconfiguration bitstream into the corresponding dynamic reconfiguration area through the FPGA configuration interface, while monitoring the component state migration, and starting the rollback mechanism when an anomaly occurs, until all components are successfully deployed.

[0059] This invention elevates dynamic refactoring from simple function replacement to an intelligent, adaptive system-level behavior by constructing a closed-loop process from resource allocation to execution monitoring. The overall framework is as follows: Figure 2As shown, four key steps are executed sequentially: First, adaptive mesh-based reconstruction region partitioning is performed to allocate appropriate "physical containers" for dynamic components; second, a component dependency model is established and a safe reconstruction sequence is generated to ensure the logical correctness of component state transitions; third, a pipelined parallel scheduling strategy is used to orchestrate reconstruction tasks, and data communication and timing calibration are performed simultaneously to minimize reconstruction latency; finally, while safely executing the reconstruction plan, real-time status monitoring and anomaly rollback mechanisms are implemented to ensure system reliability. This method significantly improves FPGA resource utilization, system reliability, and real-time performance, and is suitable for fields with extremely high real-time requirements, such as software-defined radio and adaptive signal processing.

[0060] In step S1, after placement and routing, a dynamic partitioning algorithm based on two-dimensional mesh modeling and a greedy merging strategy generates an adaptive region for each waveform component, replacing the pre-fixed functional circuit partitions.

[0061] Existing technologies directly perform global placement and routing, and statically partition the PRR boundaries. However, when faced with changing task requirements, this can easily lead to fragmentation of FPGA logic resources, making it impossible to deploy subsequent components. Therefore, this invention introduces an adaptive mesh partitioning algorithm, which treats FPGA resources as mesh cells that can dynamically adjust their size and shape. This algorithm can adaptively partition the FPGA according to the actual needs of the components to be deployed, thereby maximizing resource utilization and system deployment success rate.

[0062] Its core principle is to virtualize the FPGA's two-dimensional logic resource plane (such as SLICE, BRAM, DSP block array) into a fine-grained grid. Each grid cell has a fixed basic resource capacity, and the algorithm maintains a real-time updated grid resource state map. When allocating a region for a component, the algorithm does not search for a pre-defined fixed region, but rather dynamically searches for and merges one or more consecutive free grid cells in the grid according to the component's resource requirements, forming a rectangular region that exactly meets or is slightly larger than the component's requirements. This is similar to dynamic partitioned memory management in an operating system, but optimized for the characteristics of FPGA hardware resources. Figure 3 As shown, the specific implementation steps are as follows:

[0063] After the dynamic waveform component design is completed, the synthesis and place-and-route tools will generate a netlist and resource requirement report for each component. These reports are then parsed to extract the precise LUT, register, BRAM, and DSP usage for each component, forming a resource requirement vector. Meanwhile, through the FPGA's internal monitoring logic, the current occupancy status bitmap of all grid cells can be obtained for Xilinx's SYSMON or a user-designed scan chain.

[0064] Adaptive Mesh Partitioning and Greedy Merging Algorithm: For the current component, the algorithm first searches for a contiguous free rectangular block of sufficient area in the free region of the mesh. If found, it is directly allocated.

[0065] If not found, a greedy merging process is initiated, such as... Figure 4 As shown: Starting from a free grid cell, iteratively evaluate all its neighboring free cells. Merge decision function. Used to calculate the benefits of merging each candidate neighbor cell:

[0066] ;

[0067] in, This is a temporary merged area. To ensure the reward area is close to the required value, in order to avoid over-allocation; To reward shape regularity (such as rectangularity). To reward components that have a communication relationship with each other, the physical proximity of the components is considered. This is the area benefit weighting coefficient. For shape benefit weighting coefficients, For communication benefit weighting coefficients; algorithm selection makes Merge the largest neighboring area until the area of ​​the merged area is equal to the total area. ,in The required area is for the components. During the merging process, the rectangularity of the area shape is prioritized to facilitate layout and wiring.

[0068] Output grid layout scheme: The algorithm ultimately outputs a grid layout scheme for each component. A specific continuous grid region is allocated, and the dynamic grid layout scheme for the entire chip is output. This scheme defines the boundary coordinates of each dynamic reconfiguration region (PRR), which will guide the generation of subsequent bits streams; the grid cell size is from a minimum of 2×2 LUT granularity to a maximum of 8×8 LUT granularity.

[0069] In some examples, the algorithm receives component dependency information during layout planning. If there are components with close data interaction, they are preferentially allocated areas on the FPGA chip that are adjacent to each other or connected by high-speed vertical interconnects (such as UltraRAM, high-speed bus) to minimize interconnect latency and routing congestion, provided that their respective resource requirements are met.

[0070] This invention enables dynamic and refined allocation of FPGA logic resources, improving resource utilization by an average of over 25%. It effectively addresses scenarios with varying waveform component resource requirements and random task arrival sequences, significantly reducing the probability of reconfiguration failure due to resource fragmentation and providing a flexible physical basis for large-scale, multi-component dynamic deployment.

[0071] In step S2, the dependencies between components are modeled as a weighted directed acyclic graph (DAG). Through topological sorting and critical path analysis, a conflict-free and safe reconfiguration sequence is generated that satisfies dependency constraints while minimizing the total time.

[0072] In general, the refactoring process is directly scheduled by the Refactoring Controller (RSG). However, in practice, if component B, which depends on component A, is loaded before component A is ready, it can lead to data path errors or system deadlock. Therefore, this invention uses a Directed Acyclic Graph (DAG) model and critical path analysis to make implicit dependencies explicit and modeled. Simultaneously, a state machine is used for compliance checks, preventing runtime failures caused by dependency violations. Figure 5 As shown, the specific implementation steps are as follows:

[0073] DAG Construction: The system integration development environment or configuration management unit parses the waveform application description file, automatically extracts the input and output port connection relationships of components, and constructs an initial DAG model. Each waveform component is a node in the DAG. Data flow between components is directed by edges. Nodes can be assigned weights. The directed edge represents the estimated reconstruction time of the component and can be assigned a weight. This represents the strength of the dependency or the cost of establishing communication; weight. It can be initialized to a fixed value and supports dynamic updates during system operation based on actual component operation data (such as data throughput and communication latency), making the model closer to actual operation.

[0074] Topology sorting: A breadth-first search (BFS) algorithm is used for topology sorting. The algorithm repeatedly identifies and removes nodes with an in-degree of 0, adding them to the reconstruction sequence while updating the in-degree of their subsequent nodes. This process continues until all nodes have been processed, ultimately yielding a basic reconstruction order list that satisfies all data dependency constraints. This ensures that any component is scheduled after all its predecessor components. A node with an in-degree of 0 represents a starting node that does not depend on any other component or a node whose dependencies are all satisfied.

[0075] Critical path analysis: A dynamic programming algorithm is used to calculate the earliest start time of each node from the bottom up. and latest start time :

[0076] ;

[0077] ;

[0078] in, and These represent the sets of predecessor and successor vertices, respectively. Critical path. By all satisfying The vertices constitute the path, and they are marked as high-priority paths. Its path length This determines the shortest possible time for the refactoring.

[0079] Sequence optimization and secure injection: with Based on, Adjust the nodes forward as much as possible to form an optimized sequence. Simultaneously, a state machine is instantiated for each component (including idle, configuration, initialization, running, and error states). State checkpoints are inserted into the sequence; for example, before executing the command "load component B", a check instruction "wait for component A to enter the running state" must be inserted.

[0080] The final result is a safe refactoring sequence file. This file not only contains the component loading order but also embeds necessary state wait and verification instructions.

[0081] This invention achieves predictability and logical safety in the reconfiguration process. Through critical path optimization, the overall reconfiguration completion time can be shortened in typical scenarios. Mandatory state dependency checks eliminate system failures caused by asynchronous loading, making the dynamic reconfiguration of complex waveform systems reliable and controllable.

[0082] In step S3, the refactoring task is decoupled into multiple stages and organized into a pipeline, enabling the tasks of different components to be executed in parallel; at the same time, a high-precision timing calibration algorithm is integrated to compensate for the clock deviation introduced by the refactoring and generate an efficient and accurate refactoring execution plan.

[0083] Considering that existing technologies use serial loading, the total delay is the sum of the reconstruction times of each circuit, which is inefficient and may change the wiring, introduce clock skew, and affect the performance of high-speed interfaces; therefore, this invention uses pipelined scheduling to make the reconstruction stages of different circuits overlap, compressing the total time; and uses hardware online LMS calibration to compensate for timing errors in real time, ensuring lossless performance.

[0084] like Figure 6 As shown, the principle and implementation process are as follows:

[0085] Pipeline scheduling modeling: generating safe reconfiguration sequences Next, it needs to be transformed into a plan that can be executed efficiently. To do this, the task of reconfiguring individual circuits... The decoupling is divided into three parallelizable stages: P1: Load (configuration bitstream loading), P2: Init (component instantiation and initialization), and P3: Switch (data stream switching and synchronization). Next, the scheduling problem is formalized as a resource-constrained project scheduling problem, with the optimization objective of minimizing the total time from the start of refactoring to the readiness of all components. Mutual exclusion constraints on shared resources (such as ICAP configuration ports and global clock networks) and dependencies between tasks (as determined by…) are considered. The definition and the constraints together constitute the problem's conditions.

[0086] Scheduling algorithm integrated with LMS calibration:

[0087] List scheduling algorithm: Employs a priority-based heuristic list scheduling approach. Priority Based on the remaining time on the critical path, the algorithm iteratively schedules the next stage of the highest-priority ready task while satisfying resource constraints.

[0088] LMS timing calibration integration: For circuits requiring high-precision synchronization, a calibration subtask is inserted in stage P3. Calibration is performed by a hardware-implemented LMS filter.

[0089] set up For an ideal reference signal, For adjustable delay line The actual sampled signal has the following error signal: LMS algorithm iterative update delay To minimize mean square error Its core iterative formula is:

[0090] ;

[0091] in, It is the first The estimated time series error for the next iteration This is the step size factor, which controls the convergence speed and stability. It is the mean square error performance function about The gradient. In hardware implementation, the gradient can be obtained through the error signal. The estimation is performed by multiplying the product of the input signal's differential approximation value and the input signal. Therefore, the iteration is specifically implemented in hardware as follows:

[0092] ;

[0093] in, For the delay to be calibrated, This is the sign function, used to simplify gradient estimation. This formula drives the delay. Converging to the optimal value to eliminate clock phase deviation.

[0094] This circuit continues to operate, driving an adjustable delay line (such as the phase adjustment terminal of a digital phase-locked loop) until... If the power is below the threshold, it indicates that the clock / data phase is precisely aligned. This calibration process is pre-programmed into the refactoring execution plan and triggered by the intelligent execution engine at runtime.

[0095] The final output is a detailed refactoring execution plan that includes timestamps, resource usage, and calibration instructions.

[0096] This invention employs pipelined scheduling, which reduces the total reconstruction time to far less than that of the serial method; its adaptive timing calibration can reduce the clock uncertainty introduced by reconstruction to the picosecond level, ensuring high-speed signal processing performance.

[0097] In step S4, during the refactoring process, the intelligent engine replaces the traditional RSG to perform the refactoring according to the plan, and achieves fault detection and automatic recovery through state snapshots and real-time monitoring.

[0098] Considering the lack of monitoring and fault tolerance capabilities in traditional RSG systems, which can easily lead to system crashes due to loading failures, this invention uses state snapshots and automatic rollback mechanisms to save critical states before reconstruction, continuously monitor during reconstruction, and quickly recover in case of anomalies. This provides a robust safety net for dynamic reconstruction and enables the system to self-heal, such as... Figure 7 As shown, its management process is as follows:

[0099] State Snapshot: Before reconstructing a dynamic reconfiguration region (PRR), the engine reads and saves all user-defined key context information in that region via the system bus. This typically includes: the value of the control status register (CSR), intermediate data and coefficient tables stored in block RAM (BRAM) or distributed RAM, the current state encoding of the finite state machine (FSM), and specific I / O states for interaction with external devices.

[0100] After being compressed, this data is associated with the version identifier of the currently running configuration bitstream and stored in external non-volatile flash memory or in a "backup area" of a protected region within the chip.

[0101] Health monitoring: Real-time monitoring of CRC configuration, initialization completion signal, and the validity of function output.

[0102] Automatic rollback: When an anomaly is detected (such as timeout or verification error), the rollback process is triggered: the current task is paused, and the previous stable version bitstream is loaded until the associated state snapshot is restored, and finally a soft reset and verification are performed.

[0103] Execution Engine: As a state machine, it drives the entire process based on planning and monitoring feedback, realizing a closed loop of "planning-execution-monitoring-recovery".

[0104] The intelligent engine of this invention endows the dynamic reconfiguration system with strong inherent resilience, minimizes the impact of local faults, and reduces the time from fault occurrence to service recovery from minutes of manual diagnosis to hundreds of milliseconds, greatly improving the availability level of the entire electronic system.

[0105] On the other hand, embodiments of the present invention also provide an adaptive meshing FPGA waveform pipeline dynamic reconstruction system, which is used to implement an adaptive meshing FPGA waveform pipeline dynamic reconstruction method as described in any of the above embodiments, such as... Figure 8 As shown, it includes: a resource partitioning unit, a path planning unit, a scheduling and execution unit, and a configuration management unit, wherein:

[0106] The resource partitioning unit includes a resource status monitoring module and an adaptive mesh generation module, used to monitor the real-time distribution status of FPGA logic resources and run an adaptive mesh partitioning algorithm to generate a dynamic mesh layout scheme based on the resource requirements of the waveform components to be deployed. The path planning unit includes a DAG modeling module and a critical path analysis module, used to construct a DAG model based on the data flow and control flow dependencies between waveform components, and perform topology sorting and critical path analysis to generate a safe reconfiguration sequence. The scheduling and execution unit includes a pipeline scheduler, a timing calibration module, and a status monitoring module. The pipeline scheduler is used to orchestrate pipelined strategies and execute reconfiguration tasks based on the safe reconfiguration sequence and mesh layout scheme. The timing calibration module is used to perform data communication and timing calibration simultaneously during the reconfiguration process. The status monitoring module is used to monitor component state migration when loading partial reconfiguration bitstreams through the FPGA configuration interface and to initiate a rollback mechanism in case of anomalies. The configuration management unit includes a bitstream library management module and a version control module, used to store and manage multiple versions of partial reconfiguration bitstream files and provide the correct configuration files for the corresponding dynamic reconfiguration areas according to the reconfiguration execution plan.

[0107] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A dynamic reconstruction method for an adaptive meshed FPGA waveform pipeline, characterized in that: include: S1: Based on the resource requirements of the waveform components to be deployed and the real-time distribution status of FPGA resources, dynamically reconstruct the region and generate a grid layout scheme. S2: Construct a DAG model based on the data flow and control flow dependencies between waveform components, and perform topology sorting and critical path analysis based on the DAG model to obtain a safe reconstruction sequence; S3: Based on the security reconstruction sequence and the grid layout scheme, a pipelined parallel scheduling strategy is adopted to arrange the execution order of reconstruction tasks, and data communication and timing calibration are performed simultaneously to generate a reconstruction execution plan; S4: According to the reconfiguration execution plan, load a portion of the reconfiguration bitstream into the corresponding dynamic reconfiguration area through the FPGA configuration interface, while monitoring the component state migration, and initiating a rollback mechanism when an anomaly occurs, until all components are successfully deployed.

2. The adaptive meshing FPGA waveform pipeline dynamic reconstruction method as described in claim 1, characterized in that: The dynamic reconstruction region division adopts an adaptive grid division algorithm, which dynamically adjusts the size and shape of the grid cells according to the real-time resource requirements of each component, and maps each component to an independent grid cell; The minimum size of the grid cell is 2×2 LUT granularity, and the maximum size is 8×8 LUT granularity; If there are components with close data interaction, prioritize allocating the refactoring regions that are adjacent or have the shortest communication paths.

3. The adaptive meshing FPGA waveform pipeline dynamic reconstruction method as described in claim 2, characterized in that: The adaptive mesh partitioning algorithm also includes a greedy merging strategy: Based on the current resource requirements of the waveform components, all unoccupied and physically adjacent free grid cells are traversed in the FPGA resource layout. Calculate the combined area of ​​these adjacent free grid cells and determine whether it is greater than or equal to the minimum resource area threshold required by the current waveform component; If the combined area meets the component resource area requirements, these free grid cells are merged into a continuous rectangular region and allocated to the current waveform component for use, and the occupancy status of this region is updated in the grid layout scheme.

4. The adaptive meshing FPGA waveform pipeline dynamic reconstruction method as described in claim 1, characterized in that: The DAG model is constructed with waveform components as nodes and the data flow between components as directed edges. The edge weights of the DAG model are dynamically updated based on real-time running data, which includes one or more of the following: input rate, output rate, processing latency, logic resource utilization, and error rate.

5. The adaptive meshing FPGA waveform pipeline dynamic reconstruction method as described in claim 1, characterized in that: The topology sorting is implemented based on the data dependency constraints between nodes in the DAG model. The sequence of nodes with an in-degree of 0 is identified by the breadth-first search algorithm, and a basic reconstruction order that satisfies the predecessor-successor relationship between components is generated. The critical path analysis is achieved by summing the node reconstruction time and the edge communication overhead. The longest path in the DAG is calculated using a dynamic programming algorithm to determine the sequence of critical components with the largest total time consumption during the reconstruction process. The overall reconstruction time is optimized by prioritizing the scheduling of components on the critical path.

6. The adaptive meshing FPGA waveform pipeline dynamic reconstruction method as described in claim 1, characterized in that: The pipelined parallel scheduling strategy divides the reconstruction process into multiple independent stages and assigns the reconstruction tasks of different grid units to the independent stages for parallel execution. The timing calibration is based on the phase adjustment function of the FPGA's internal clock management unit to fine-tune the relative timing of the configuration data loading, or to achieve timing synchronization between multiple modules by inserting a programmable number of synchronization wait cycles.

7. The adaptive meshing FPGA waveform pipeline dynamic reconstruction method as described in claim 6, characterized in that: The timing calibration is achieved through a least mean square adaptive filtering algorithm, dynamically adjusting parameters based on the real-time monitored timing error. The calibration formula is as follows: ； in, It is the first The estimated time series error for the next iteration This is the step size factor, which controls the convergence speed and stability. It is the mean square error performance function about The gradient.

8. The adaptive meshing FPGA waveform pipeline dynamic reconstruction method as described in claim 1, characterized in that: The state transition of the monitoring component includes: Before reconstruction, the register values ​​and memory state of the dynamic region are saved to a dedicated backup area through a state snapshot mechanism; After refactoring, monitor the validity of the ready signals and output data of the monitoring components.

9. The adaptive meshing FPGA waveform pipeline dynamic reconstruction method as described in claim 8, characterized in that: After the rollback mechanism is triggered, the FPGA configuration interface controls the configuration of the abnormal dynamic region to roll back to the previous known stable version, and uses the state snapshot to restore the operating state of the components in that region. The anomalies include configuration frame verification errors, component initialization timeouts, or abnormal function outputs.

10. An adaptive meshing FPGA waveform pipeline dynamic reconstruction system, the system being used to implement the adaptive meshing FPGA waveform pipeline dynamic reconstruction method as described in any one of claims 1-9, characterized in that: include: The resource partitioning unit includes a resource status monitoring module and an adaptive mesh generation module, used to monitor the real-time distribution status of FPGA logic resources and, based on the resource requirements of the waveform components to be deployed, run an adaptive mesh partitioning algorithm to generate a dynamic mesh layout scheme; the path planning unit includes a DAG modeling module and a critical path analysis module, used to construct a DAG model based on the data flow and control flow dependencies between waveform components, and perform topology sorting and critical path analysis to generate a safe reconfiguration sequence; the scheduling and execution unit includes a pipeline scheduler, a timing calibration module, and a status monitoring module, wherein: the pipeline scheduler is used to orchestrate pipelined strategies and execute reconfiguration tasks based on the safe reconfiguration sequence and the mesh layout scheme; The timing calibration module is used to perform data communication and timing calibration simultaneously during the reconstruction process; the status monitoring module is used to monitor component status migration when loading a partial reconfiguration bitstream through the FPGA configuration interface, and to initiate a rollback mechanism in case of an anomaly; the configuration management unit includes a bitstream library management module and a version control module, which are used to store and manage multiple versions of partial reconfiguration bitstream files, and to provide the correct configuration file for the corresponding dynamic reconstruction area according to the reconstruction execution plan.

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