Multi-level time sequence optimization logic method for FPGA (Field Programmable Gate Array) high-frequency data acquisition and processing

Abstract

The invention relates to the technical field of data acquisition and processing, and discloses a multi-level time sequence optimization logic method for FPGA high-frequency data acquisition and processing, which can significantly break through the performance bottleneck of a traditional static hardware architecture in processing non-stationary high-frequency signals, and can improve the performance of the FPGA high-frequency data acquisition and processing by introducing a dynamic value evaluation system based on signal entropy. According to the method, accurate capture and priority scheduling of key mutation data are achieved, occupation of invalid data on computing resources is thoroughly eliminated, and by means of time sequence and resource coupled topology planning and an elastic assembly line reconstruction technology, hardware logic has the self-adaptive capacity of real-time deformation along with data features, and the real-time performance of the hardware logic is improved. Not only are time sequence conflicts and metastable state risks effectively avoided under extreme working conditions, but also self-evolution and calibration of system parameters are realized through a closed-loop feedback mechanism. And the utilization efficiency of logic resources and the robustness of multi-task parallel processing are remarkably improved.

Description

Technical Field

[0001] This application relates to the field of data acquisition and processing technology, and in particular to a multi-level timing optimization logic method for FPGA high-frequency data acquisition and processing. Background Technology

[0002] With the rapid development of intelligent manufacturing and industrial internet technologies, modern large-scale industrial control systems are evolving from simple automation to deep intelligence and high precision. In complex application scenarios such as aerospace testing, precision CNC machining, high-speed rail transit monitoring, and fault diagnosis of large rotating machinery, systems need to achieve all-weather, nanosecond-level precise perception of the physical world. To capture transient physical changes, massive numbers of high-frequency sensors deployed on-site, such as piezoelectric vibration sensors, fiber optic temperature sensors, and high-dynamic pressure sensors, continuously generate real-time data streams with extremely high sampling rates and wide dynamic ranges. This data is not only enormous in volume but also often contains key characteristic information characterizing the health status of equipment or control deviations. Therefore, building a data acquisition and processing platform capable of handling such high throughput and possessing extremely low latency response capabilities has become a core infrastructure for ensuring the safe and stable operation of industrial systems, achieving predictive maintenance, and closed-loop precision control, directly determining the operational efficiency and safety boundaries of high-end equipment.

[0003] However, in the existing high-frequency data acquisition and processing technology system, although Field-Programmable Gate Arrays (FPGAs) have gradually replaced general-purpose processors as the mainstream choice for processing high-bandwidth signals due to their parallel computing architecture and customizable hardware logic, they still face severe technical bottlenecks when dealing with non-stationary, highly dynamic, and complex timing signals. Existing FPGA logic designs generally employ static timing analysis methods, meaning that logic synthesis and placement / routing are completed during the design phase based on preset worst-case scenarios. Once the bitstream file is loaded, its internal logic connections, pipeline depth, and resource allocation patterns are permanently fixed. This static and rigid hardware architecture cannot perceive the characteristics and urgency of real-time changes in external input signals. When the sensor data stream experiences sudden high-frequency oscillations or mode transitions, the fixed logic path often cannot provide sufficient timing margin, leading to insufficient critical path setup time and thus causing data metastability or bit errors. Conversely, when processing steady-state signals, the excessive reserved redundant resources result in a huge waste of computing power and power consumption. This spatiotemporal mismatch between hardware resource supply and dynamic data timing requirements makes it difficult for existing systems to simultaneously ensure high reliability of data processing and high efficiency of resource utilization in millisecond-level transient changes, severely restricting the response speed and robustness of high-frequency data acquisition systems under extreme conditions. Summary of the Invention

[0004] This application proposes a multi-level timing optimization logic method for FPGA high-frequency data acquisition and processing to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, this application adopts the following technical solution: a multi-level timing optimization logic method for FPGA high-frequency data acquisition and processing, comprising the following steps:

[0006] Step S1: Acquire multi-channel sensor signals through the FPGA parallel input interface, calculate the signal change rate and information entropy within the local time window of the multi-channel sensor signals, generate a timing urgency index representing the processing priority of each channel based on the calculation results, divide the multi-channel sensor signals into different processing levels according to the timing urgency index, and output a weighted data stream vector containing priority identifiers.

[0007] Step S2: Receive the weighted data stream vector output from step S1, predict the timing conflict risk of the critical path based on the timing urgency index, and search for the global optimal solution in the 3D logic resource grid of the FPGA using the timing-resource coupling cost function. The timing-resource coupling cost function is used to quantify the correlation between logic delay, routing delay and resource contention heat, thereby generating the optimal logic layout configuration bit stream that guides the hardware resource connection relationship and the timing constraint boundary set that defines the timing safety range.

[0008] Step S3: Load the optimal logic layout configuration bit stream generated in step S2, dynamically connect and reconstruct the configurable logic blocks and digital signal processing units inside the FPGA, and automatically insert pipeline registers in logic paths with insufficient setup time margins based on the timing prediction results in the optimal logic layout configuration bit stream, perform data feature extraction operations, and output execution performance indicators.

[0009] Step S4: Compare the execution performance index output in step S3 with the timing constraint boundary set generated in step S2. When the execution performance index is detected to deviate from the timing constraint boundary set, calculate the feedback compensation parameter and transmit it back to step S1. Use the feedback compensation parameter to update the calculation weight of the timing urgency index to complete the closed-loop timing calibration.

[0010] Furthermore, the steps in step S1 for calculating the signal change rate of the multi-channel sensor signal and the information entropy within the local time window specifically include:

[0011] The configurable input / output module of the field-programmable gate array receives differential signals from multiple channels, uses an internal hardware differentiator to calculate the first-order and second-order differences of the sampled values ​​in the current clock cycle, and assembles the sampled values, first-order differences, and second-order differences into a local state vector.

[0012] Subsequently, the embedded digital signal processing slice performs gradient-based spatiotemporal entropy evolution operation on the local state vector. This operation quantifies the complexity of the signal waveform by analyzing the sparsity of the distribution of the second-order difference within the local time window, thereby replacing the statistical variance calculation and capturing the nonlinear abrupt changes in the signal.

[0013] Furthermore, step S1, which generates a timing urgency index representing the processing priority of each channel based on the calculation results and outputs a weighted data stream vector containing priority identifiers, specifically includes:

[0014] The timing urgency index is calculated using nonlinear weighted fusion logic, which consists of three dimensions: a local gradient generalized entropy term derived from the signal change rate to characterize the intensity of signal evolution; a nonlinear instantaneous impact term derived from the degree of deviation of the sampling point from the local median to characterize abnormal amplitude jumps; and a time starvation aging factor term that grows exponentially with the channel service waiting time to prevent data deadlock.

[0015] After obtaining the timing urgency index, it is compared with a preset set of dynamic thresholds to determine the processing level state of the signal, and the label of this state is encapsulated into the original data frame header to construct a weighted data stream vector for subsequent steps.

[0016] Furthermore, step S2, which involves predicting the temporal conflict risk of the critical path based on the temporal urgency index and constructing a 3D resource mesh and virtual potential field, specifically includes:

[0017] Read the physical layout data of the field-programmable gate array and establish the mapping relationship between physical logic units and nodes in a three-dimensional discrete coordinate system;

[0018] Based on the temporal urgency index output in step S1, a virtual potential field is constructed in a three-dimensional discrete coordinate system. Logical nodes associated with tasks with high temporal urgency index are defined as high gravitational potential wells with adsorption effects, so as to forcibly guide the surrounding physical interconnection resources to gather towards the node. At the same time, logical nodes associated with tasks with low temporal urgency index are defined as low potential energy regions, thereby pre-setting a topology structure with the shortest Manhattan distance for the critical path at the physical layout level.

[0019] Furthermore, step S2, which involves searching for the globally optimal solution in the FPGA's three-dimensional logic resource grid using the timing-resource coupling cost function, specifically includes:

[0020] A time-resource coupling cost function with three evaluation dimensions is constructed, and the minimum value of the function is solved using an iterative optimization algorithm;

[0021] The three evaluation dimensions are: the urgency-weighted delay rate dimension, which uses the temporal urgency index as a weight to nonlinearly penalize the total physical path delay; the resource topology space dispersion dimension, which is used to quantify and constrain the degree of dispersion of logical units in physical space; and the dynamic spatiotemporal conflict overlap integral dimension, which is calculated by accumulating the conflict intensity of multiple tasks occupying the same resource in the continuous time domain.

[0022] Finally, the optimal logical layout configuration bitstream is generated based on the globally optimal resource allocation scheme found, and the set of timing constraint boundaries is derived in reverse.

[0023] Furthermore, step S3, which involves loading the optimal logic layout configuration bitstream and dynamically reconstructing the configurable logic blocks and digital signal processing units within the FPGA, specifically includes:

[0024] The system calls the dynamic reconfigurable interface of the field-programmable gate array or the preset multiplexer network, and, while maintaining the continuity of the global clock signal, parses the routing instructions in the optimal logic layout configuration bit stream, and switches the state of the input and output routing switch matrix of the configurable logic block and digital signal processing slice.

[0025] Simultaneously, currently idle computing resources are retrieved and dynamically allocated to data processing channels with high time urgency indices to construct dedicated parallel computing arrays.

[0026] Furthermore, step S3, which involves configuring the timing prediction results in the bit stream based on the optimal logic layout, and automatically inserting pipeline registers into logic paths with insufficient setup time margins, specifically includes:

[0027] An online timing detection mechanism is activated to monitor the signal propagation delay of critical logic paths in real time. When the establishment time margin of a certain logic path is detected to be lower than the preset safety threshold, an elastic pipeline deep modulation strategy is executed.

[0028] This strategy determines the optimal number of registers to be inserted by solving the extrema of a discrete optimization function. The discrete optimization function consists of a delay cost penalty term weighted by the timing urgency index and a timing robustness gain term calculated based on the setup time margin loss and the variance of the ambient clock jitter.

[0029] Finally, based on the calculated optimal number of registers to be inserted, the pre-embedded shadow registers are activated at the key nodes of the combinational logic chain to cut the long combinational logic path into multiple short paths.

[0030] Furthermore, in step S4, the execution performance metric output in step S3 is compared with the time-series constraint boundary set generated in step S2. The specific steps for detecting when the execution performance metric deviates from the time-series constraint boundary set include:

[0031] The execution time-difference diagnostic logic calculates the difference between the actual path delay vector and instantaneous data throughput contained in the execution performance indicators and the time-constraint boundary set used as the benchmark reference system, and generates a time-difference tensor to characterize the degree of deviation.

[0032] Simultaneously, the on-chip temperature sensor or flip-flop rate monitor is invoked to physically detect logic hotspots. If the value in the timing deviation tensor exceeds the preset tolerance threshold or the flip-flop rate of the logic region is abnormal, a switching instruction is immediately generated to activate the redundant logic path reserved in step S2 for seamless takeover, and the timing deviation tensor is marked as a parameter mismatch signal and transmitted to the subsequent calculation stage.

[0033] Furthermore, the step of calculating the feedback compensation parameters in step S4 specifically includes:

[0034] A comprehensive evaluation model based on the coupling of the generalized logistic function and the exponential penalty term is constructed. This model is used to calculate the system robustness and cooperative gain coefficients used to quantify the effectiveness of the current system parameter configuration. The comprehensive evaluation model consists of three nonlinear components:

[0035] The throughput saturation gain component is calculated using the ratio of actual throughput to baseline throughput; the timing deviation penalty component is calculated based on the maximum absolute norm of the timing deviation tensor; and the resource efficiency logarithmic gain component reflects the resource saving ratio.

[0036] By performing a product operation on the above three nonlinear components, the normalized system robustness and cooperative gain coefficients are obtained.

[0037] Furthermore, step S4, which involves updating the calculation weights of the timing urgency index using feedback compensation parameters and completing closed-loop timing calibration, specifically includes:

[0038] A gradient-based adaptive update strategy is adopted to generate a feedback compensation parameter vector based on the calculated system robustness and cooperative gain coefficient.

[0039] The weight coefficients of each dimension used to calculate the time urgency index in step S1 are incrementally corrected using the feedback compensation parameter vector. The weight coefficients include the entropy weight coefficient corresponding to the generalized entropy term of the local gradient, the impact weight coefficient corresponding to the nonlinear instantaneous impact term, and the aging weight coefficient corresponding to the time starvation aging factor term.

[0040] During the correction process, if the system robustness and cooperative gain coefficient are in the high range, a small steady-state fine-tuning is performed.

[0041] If the system robustness and synergistic gain coefficient are in the low range, a significant parameter reshaping is performed to enable the system to adaptively evolve in response to changes in the industrial environment.

[0042] The beneficial effects of this invention are as follows:

[0043] This invention significantly overcomes the performance bottleneck of traditional static hardware architectures when processing non-stationary high-frequency signals. By introducing a dynamic value assessment system based on signal entropy, it achieves accurate capture and priority scheduling of key abrupt changes in data, completely eliminating the occupation of computing resources by invalid data. This method utilizes topology planning and elastic pipeline reconfiguration technology that couples timing and resources, enabling the hardware logic to adapt to real-time deformations with data characteristics. It not only effectively avoids timing conflicts and metastability risks under extreme conditions, but also achieves self-evolution and calibration of system parameters through a closed-loop feedback mechanism. This dynamic optimization mechanism that combines software and hardware significantly reduces system response latency while significantly improving the utilization efficiency of logic resources and the robustness of multi-task parallel processing, ensuring the all-weather, high-precision, and stable operation of industrial control systems in complex electromagnetic environments. Attached Figure Description

[0044] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort:

[0045] Figure 1 This is a diagram illustrating the overall framework of the method of the present invention;

[0046] Figure 2 This is a logic diagram of step 1 of the present invention;

[0047] Figure 3 This is a logic diagram of step 2 of the present invention;

[0048] Figure 4 This is a logic diagram of step 3 of the present invention;

[0049] Figure 5 This is a logic diagram of step 4 of the present invention. Detailed Implementation

[0050] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0051] Example 1, as Figure 1As shown, a multi-level timing optimization logic method for FPGA high-frequency data acquisition and processing includes the following steps:

[0052] Step S1: Acquire multi-channel sensor signals through the FPGA parallel input interface, calculate the signal change rate and information entropy within the local time window of the multi-channel sensor signals, generate a timing urgency index representing the processing priority of each channel based on the calculation results, divide the multi-channel sensor signals into different processing levels according to the timing urgency index, and output a weighted data stream vector containing priority identifiers.

[0053] Step S2: Receive the weighted data stream vector output from step S1, predict the timing conflict risk of the critical path based on the timing urgency index, and search for the global optimal solution in the 3D logic resource grid of the FPGA using the timing-resource coupling cost function. The timing-resource coupling cost function is used to quantify the correlation between logic delay, routing delay and resource contention heat, thereby generating the optimal logic layout configuration bit stream that guides the hardware resource connection relationship and the timing constraint boundary set that defines the timing safety range.

[0054] Step S3: Load the optimal logic layout configuration bit stream generated in step S2, dynamically connect and reconstruct the configurable logic blocks and digital signal processing units inside the FPGA, and automatically insert pipeline registers in logic paths with insufficient setup time margins based on the timing prediction results in the optimal logic layout configuration bit stream, perform data feature extraction operations, and output execution performance indicators.

[0055] Step S4: Compare the execution performance index output in step S3 with the timing constraint boundary set generated in step S2. When the execution performance index is detected to deviate from the timing constraint boundary set, calculate the feedback compensation parameter and transmit it back to step S1. Use the feedback compensation parameter to update the calculation weight of the timing urgency index to complete the closed-loop timing calibration.

[0056] Example 2, as Figure 2 As shown, the steps in step S1 for calculating the signal change rate of the multi-channel sensor signal and the information entropy within the local time window specifically include: controlling the configurable input / output module of the field-programmable gate array (FPGA) to receive the differential signals from the multi-channel signals, using the internal hardware differentiator to calculate the first-order and second-order differences of the sampled values ​​in the current clock cycle, and assembling the sampled values, the first-order differences, and the second-order differences together into a local state vector. Specifically, the FPGA accesses the sensor in parallel through its configurable I / O modules (IOBs). The physical channel of the sensor, for the first The first channel in the Sample value at time ( The logic control unit does not directly cache the data; instead, it utilizes the lookup table resources within the FPGA to construct a hardware differentiator. This differentiator calculates in real-time the first derivative (i.e., the rate of change) and the second derivative (i.e., the acceleration) of the sampled value with respect to time. Subsequently, the system will use the sampled value at the current moment ( The first and second derivatives are vectorized and assembled to construct a local state vector containing multidimensional dynamic features. This process ensures that the data possesses the basic characteristics to describe the transient change trend of the signal before entering subsequent complex logical operations, providing rich data dimensions for subsequent entropy value analysis.

[0057] Following the above operations, the embedded digital signal processing slice is then driven to perform gradient-based spatiotemporal entropy evolution calculations on the local state vector. This operation quantifies the complexity of the signal waveform by analyzing the sparsity of the second-order difference distribution within the local time window, thereby replacing statistical variance calculations and capturing the nonlinear abrupt changes present in the signal. During this process, the FPGA calls the internal embedded digital signal processing slice (DSPSlice) to perform gradient-based spatiotemporal entropy evolution calculations on the local state vector (DSPSlice). This approach performs in-depth analysis. Unlike the mean or variance calculations commonly used in existing technologies, this implementation focuses on the "unpredictability" of the signal waveform. The system uses a sliding window mechanism to analyze the sparsity of the second derivative distribution along the time axis. If the second derivative is uniformly distributed, it indicates that the signal is in a steady state or undergoing linear changes; if the second derivative exhibits sparse and drastic fluctuations, it indicates that the signal has nonlinear abrupt changes. Through this gradient-based spatiotemporal entropy evolution calculation, the system can keenly capture effective pulse signals lurking in high-frequency noise, thereby quantifying the complexity of the signal waveform and providing a precise physical basis for subsequently determining the processing priority of the data.

[0058] The step S1, which generates a timing urgency index representing the processing priority of each channel based on the calculation results and outputs a weighted data stream vector containing priority identifiers, specifically includes: calculating the timing urgency index using nonlinear weighted fusion logic. This nonlinear weighted fusion logic consists of three dimensions: a local gradient generalized entropy term derived from the signal change rate to characterize the severity of signal evolution; a nonlinear instantaneous impact term derived from the degree of deviation of the sampling point from the local median to characterize abnormal amplitude jumps; and a time starvation aging factor term that grows exponentially with the channel service waiting time to prevent data deadlock. To implement this logic, this embodiment introduces the following innovative calculation formula to generate the timing urgency index. The first channel in the The temporal urgency index of a moment ( ):

[0059] ;

[0060] In this formula:

[0061] The timing urgency index, mentioned earlier, is used as an output to quantify the urgency of the current channel processing task.

[0062] : Represents the historical second-order difference value within the local time window, used to calculate the local gradient generalized entropy term of the first dimension.

[0063] : Represents the entropy order control parameter (range of values) ), used to adjust the sensitivity to signal sparsity.

[0064] : Represents the local mean, used as a baseline.

[0065] : Represents the absolute deviation of the local median, used to provide a robust measure of dispersion when calculating the nonlinear instantaneous impact term in the second dimension, and is more resistant to outlier noise than the standard deviation.

[0066] : Represents the impact normalization coefficient, used in conjunction with the hyperbolic tangent function. Map the impact magnitude to a reasonable range.

[0067] : Represents the lag time counter, which is the number of clock cycles that have elapsed since the channel was last served. It is used to calculate the time-starvation aging factor in the third dimension.

[0068] : Represents the aging rate coefficient, which determines the rate of increase with waiting time. The speed of growth.

[0069] : These represent the entropy weight coefficient, impact weight coefficient, and aging weight coefficient, respectively, which are dynamically adjusted by the feedback mechanism in subsequent steps.

[0070] The core function of this formula in this implementation is to calculate a unique, dynamically changing scalar value by fusing the complexity (entropy term), instantaneous impact (impact degree term), and waiting time (aging term) of the signal. This solves the problem that traditional polling mechanisms cannot handle sudden high-frequency signals, and also prevents data deadlock in low-dynamic channels through the aging factor, achieving a non-linear mapping of acquisition priority.

[0071] After obtaining the timing urgency index, it is compared with a preset set of dynamic thresholds to determine the processing level state of the signal. This state is then encapsulated in the original data frame header to construct a weighted data stream vector for subsequent steps. Specifically, during execution, the logic comparator will use the calculated timing urgency index (…) ) and the preset set of dynamic thresholds ( Real-time comparison will be performed. If... A state above the mutation threshold is marked as a "high-frequency mutation state"; a state below the noise threshold is marked as a "noise state"; and a state in between is marked as a "steady state". Ultimately, the system labels this state and... The numerical values ​​are encapsulated in the header of the original data frame to form a weighted data stream vector. The vector ( It carries clear priority information, which serves as the sole input for the subsequent step S2 to perform resource topology mapping, thereby achieving a seamless connection from physical signal perception to logical resource planning.

[0072] Example 3, as Figure 3 As shown, step S2, which predicts the timing conflict risk of the critical path based on the timing urgency index and performs 3D resource meshing and virtual potential field construction, specifically includes: reading the physical layout data of the field-programmable gate array (FPGA) and establishing the mapping relationship between physical logic units and nodes in a 3D discrete coordinate system. In this process, the system first parses the hardware description file (such as an XDC file or device physical view) of the FPGA chip, abstracting the massive number of configurable logic blocks (CLBs), digital signal processing slices (DSPSlices), and block random access memory (BRAM) inside the chip. The system constructs a 3D discrete coordinate system, mapping the aforementioned physical resources to a set of nodes in the coordinate system. Each node not only records its geometric location but also associates its resource type and interconnection attributes, thereby transforming the complex hardware physical structure into a mathematical grid model that the algorithm can process.

[0073] Next, based on the temporal urgency index output in step S1, a virtual potential field is constructed in a three-dimensional discrete coordinate system. Logical nodes associated with tasks with high temporal urgency indices are defined as high-gravitational potential wells with an attraction effect, forcibly guiding surrounding physical interconnect resources to converge towards these nodes. Simultaneously, logical nodes associated with tasks with low temporal urgency indices are defined as low-potential regions, thus pre-setting a topology with the shortest Manhattan distance for the critical path at the physical layout level. In specific implementation, the system utilizes the temporal urgency index (…) in the weighted data flow vector generated in step S1… This serves as the basis for setting the potential field strength. For For critical tasks with extremely high values, the system sets the target logical nodes it maps to as "high-gravity potential wells." During the initialization phase of the placement algorithm, this potential well effect generates a mathematical "gravity," forcing related logical resources (such as associated LUTs and registers) to move closer to the center of the potential well, thereby physically minimizing interconnect lengths (i.e., Manhattan distance). Conversely, low... Tasks are pushed to "low-potential regions" or chip edges. This mechanism establishes a physical advantage for the critical path in terms of topology before routing occurs, effectively avoiding timing violations caused by long-line driving in traditional layouts.

[0074] The step S2, which uses the timing-resource coupling cost function to search for the global optimum in the FPGA's three-dimensional logic resource grid, specifically includes: constructing a timing-resource coupling cost function containing three evaluation dimensions, and using an iterative optimization algorithm to find the minimum value of this function. To achieve this global search, this implementation constructs the following timing-resource coupling cost function ( This function is a multi-objective optimization functional:

[0075] ;

[0076] In this formula:

[0077] : Represents the total cost of the current logical layout mapping scheme, which serves as the objective function of optimization algorithms (such as simulated annealing or particle swarm optimization) to find the global minimum of this value.

[0078] : That is, the time urgency index passed in step S1, which is used as a weighting coefficient here to force the algorithm to prioritize optimizing the path delay of high-priority tasks.

[0079] : Indicates along the signal path The line integral, where This is the wiring delay density per unit length, used to accurately calculate the total delay generated by the physical path.

[0080] : Indicates task The theoretical maximum allowable delay is used to normalize the delay term.

[0081] : Represents the delay sensitivity index ( ), used to apply nonlinear penalties to paths approaching the time-critical point.

[0082] : Indicates task The Frobenius norm of the spatial distribution tensor of the occupied logical resources is used to quantify the spatial dispersion of logical resources.

[0083] : indicates Dirac A function whose value is infinite if and only if two tasks occupy the same resource coordinate at the same time, used to capture instantaneous resource collisions.

[0084] : Represents the coupling weight function, combining the two conflicting sides. The value is used to calculate the severity of the collision.

[0085] : These represent the resource dispersion penalty coefficient and the conflict penalty coefficient, respectively.

[0086] The core function of this formula in this implementation is that it no longer seeks a single "shortest path," but rather integrates three dimensions: "speed" (the first term), "area / clustering" (the second term), and "conflict-free concurrency" (the third term). In particular, the conflict integral in the third term enables the algorithm to perceive and avoid dynamic resource contention during multi-task parallelism.

[0087] The three evaluation dimensions are: a urgency-weighted latency rate dimension, which uses a temporal urgency index as a weight to non-linearly penalize the total physical path delay; a resource topology space dispersion dimension, which quantifies and constrains the degree of dispersion of logical units in physical space; and a dynamic spatiotemporal conflict overlap integral dimension, which accumulates the conflict intensity of multiple tasks occupying the same resource in a continuous time domain. These three dimensions correspond to the three terms in the above formula, respectively ensuring fast response of critical tasks, compact logical layout, and interference-free parallel execution.

[0088] Finally, based on the globally optimal resource allocation scheme found, the optimal logical layout configuration bitstream is generated, and the time-constraint boundary set is derived in reverse. When the optimization algorithm converges, the system locks the current mapping matrix ( ), and compile it into an optimal logic layout configuration bitstream that is recognizable by the FPGA ( This bitstream contains the LUT configuration information and the on / off status of the routing switch matrix. Simultaneously, based on the optimized path margin, the system inversely calculates the set of timing constraint boundaries under this configuration. ), including the creation time ( ) and holding time ( The dynamic security threshold provides a rigid benchmark for the subsequent reconstruction in step S3 and the closed-loop verification in step S4.

[0089] Example 4, as Figure 4As shown, the steps in step S3, which involve loading the optimal logic layout configuration bitstream and dynamically reconfiguring the configurable logic blocks and digital signal processing units within the FPGA, specifically include: calling the dynamic partial reconfigurable interface of the field-programmable gate array (FPGA) or a pre-set multiplexer network; parsing the routing instructions in the optimal logic layout configuration bitstream while maintaining the continuity of the global clock signal; and switching the state of the input / output routing switch matrices of the configurable logic blocks and digital signal processing slices. In specific implementation, the system loads the optimal logic layout configuration bitstream generated in step S2 (… This implementation does not rely on traditional full-chip shutdown and reprogramming. Instead, it utilizes the Dynamic Partial Reconfiguration (RPR) interface (such as ICAP) within the FPGA (Field-Programmable Gate Array) or a pre-built multiplexer network (MUX Network) during the design phase. The logic control module parses the routing instructions in the bit stream in real time and quickly modifies the input / output routing switch matrices around the Configurable Logic Block (CLB) and Digital Signal Processing Slice (DSP Slice) within millisecond intervals without interrupting the system's global clock, thereby physically changing the connection topology between logic units.

[0090] Simultaneously, idle computing resources are retrieved and dynamically allocated to data processing channels with high time urgency indices to construct dedicated parallel computing arrays. During this process, the resource scheduler uses the time urgency index passed in step S1 (… The system identifies DSP resources that are currently idle or under low load. Based on bitstream instructions, the system dynamically assigns these "remaining computing power" to high-load areas. On the data path of the value, a dedicated parallel computing array is constructed through parallel computing units to ensure that high-priority feature extraction tasks can achieve the maximum hardware speedup.

[0091] In step S3, based on the optimal logical layout configuration of the timing prediction results in the bit stream, the step of automatically inserting pipeline registers in logical paths with insufficient setup time margins specifically includes: activating an online timing probing mechanism to monitor the signal propagation delay of critical logical paths in real time; and executing an elastic pipelined deep modulation strategy when the setup time margin of a logical path is detected to be lower than a preset safety threshold. After the logical connection reconstruction is completed, the system immediately activates online timing probing to compare the actual delay on the critical path with the timing constraint boundary set provided in step S2 in real time. Once it is detected that the establishment time margin (Slack) of a logical path is lower than the preset security threshold (i.e., The system determines that the path has a metastability risk and then triggers the elastic pipeline deep modulation strategy.

[0092] This strategy determines the optimal number of register insertions by solving for the extrema of a discrete optimization function. The discrete optimization function consists of a delay penalty term weighted by a timing urgency index and a timing robustness gain term calculated based on setup time margin loss and ambient clock jitter variance. To accurately balance delay and stability, this implementation introduces the following flexible pipeline depth modulation function (…). This function employs the Lagrange relaxation technique for discrete optimization.

[0093] ;

[0094] In this formula:

[0095] As output, this indicates the result for the th... The optimal number of pipeline register insertions (stages) calculated for each critical path.

[0096] : Represents the insertion level variable to be optimized.

[0097] : Indicates the system clock cycle.

[0098] : Represents the timing urgency index of step S1, used as the denominator here, meaning the higher the priority, the less additional delay ( The greater the penalty, the more likely it is to maintain low latency.

[0099] : Indicates a priority sensitivity factor.

[0100] : Indicates the original setup time margin of the path before the pipeline is inserted (usually a negative value or close to zero).

[0101] : Indicates the timing compensation gain brought by a single-stage pipeline (i.e., the margin improvement obtained by shortening the logic chain).

[0102] : Represents the variance of ambient clock jitter, used to characterize the impact of noise.

[0103] : Indicates the risk penalty coefficient.

[0104] The exponential term represents the timing robustness gain. When the margin is insufficient, this term is extremely high, forcing an increase. To repair timing; when the margin is sufficient, this item quickly approaches zero.

[0105] The core function of this formula in this implementation is that it intelligently calculates the minimum latency cost required to eliminate timing violations. For high... For tasks, the algorithm will try to minimize the number of insertion levels to ensure real-time performance, unless the timing risk (determined by jitter variance and margin loss) is extremely high.

[0106] Finally, based on the calculated optimal number of register insertions, pre-embedded shadow registers are activated at key nodes of the combinational logic chain, dividing the long combinational logic path into multiple shorter paths. The system then calculates... The value automatically locates the critical cut set in the combinational logic chain and activates the pre-embedded shadow registers at that location. This operation physically cuts the originally excessively long combinational logic path into multiple shorter paths, distributing the computational pressure of a single cycle to... Within a clock cycle, this sacrifices a tiny latency in exchange for absolute system stability at high-frequency clocks.

[0107] Example 5, as Figure 5 As shown, in step S4, the execution performance index output in step S3 is compared with the time constraint boundary set generated in step S2. The specific steps when the execution performance index deviates from the time constraint boundary set include: executing the time difference diagnostic logic, calculating the difference between the actual path delay vector and instantaneous data throughput contained in the execution performance index and the time constraint boundary set as the benchmark reference system, and generating a time deviation tensor to characterize the degree of deviation.

[0108] In this embodiment, the system first obtains the actual path delay vector from step S3. ) and instantaneous data throughput ( ) execution performance metrics ( Simultaneously, the time-series constraint boundary set generated in step S2 is invoked ( This serves as a rigid benchmark reference. Instead of performing simple threshold comparisons, the logic module executes differential diagnostics: calculating the difference between the actual delay and the theoretical boundary, generating a high-dimensional timing bias tensor. This tensor not only records the delay exceeding the limit for a single path, but also maps the distribution density of the deviation in the chip's physical space.

[0109] Simultaneously, the on-chip temperature sensor or flip-flop rate monitor is invoked to physically detect logic hotspots. If the value in the timing deviation tensor exceeds the preset tolerance threshold or the flip-flop rate of the logic region is abnormal, a switching instruction is immediately generated to activate the redundant logic path reserved in step S2 for seamless takeover, and the timing deviation tensor is marked as a parameter mismatch signal and transmitted to the subsequent calculation stage.

[0110] The system monitors the internal physical state of the FPGA (Field-Programmable Gate Array) in real time. If the timing deviation tensor ( If the maximum modulus value in the logic exceeds a preset tolerance threshold (i.e., an unacceptable timing drift has occurred), or if the on-chip temperature sensor / flip-ratio monitor detects excessively rapid heat buildup in a logic region (posing a risk of single-event upset or thermal breakdown), the system will trigger a predictive fault-tolerant mechanism. At this time, the control logic immediately generates a switching instruction, activating the redundant shadow logic pre-configured in a non-critical area during the layout phase in step S2. The data flow will seamlessly migrate to this redundant path on the next clock edge, thus physically mitigating the fault. Simultaneously, The signal is marked as a "parameter mismatch signal," indicating that the current logical configuration is no longer suitable for the physical environment and requires parameter calibration at the algorithm level in subsequent stages.

[0111] The steps for calculating the feedback compensation parameters in step S4 specifically include: constructing a comprehensive evaluation model based on the coupling of the generalized logistic function and the exponential penalty term, and using this model to calculate the system robustness and cooperative gain coefficients used to quantify the effectiveness of the current parameter configuration of the system.

[0112] To accurately evaluate the overall health of the system, this implementation method constructs the following comprehensive evaluation model to calculate the system robustness and synergistic gain coefficient. ):

[0113] ;

[0114] In this formula:

[0115] The system robustness and cooperative gain coefficient (normalized to the 0~1 range) mentioned above are the output results. They are the only benchmark for measuring whether the current state of the system is optimal.

[0116] : indicates the first The actual instantaneous data throughput per cycle.

[0117] : Indicates the baseline design throughput of the system.

[0118] : Represents the generalized logistic function (a variant of the sigmoid), used to map throughput metrics to nonlinear gains, preventing a single metric from masking other defects.

[0119] : Represents the infinite norm (i.e., the maximum deviation value) of the timing deviation tensor, used to capture the timing violation of the most severe critical path.

[0120] : Represents the bias sensitivity coefficient, used to control the penalty for timing errors.

[0121] : Indicates the amount of logical resources saved compared to static configuration.

[0122] : Indicates the total available resources of the FPGA.

[0123] : Represents the resource gain index.

[0124] The comprehensive evaluation model consists of three nonlinear components: the throughput saturation gain component calculated using the ratio of actual throughput to benchmark throughput, the timing deviation penalty component calculated based on the maximum absolute norm of the timing deviation tensor, and the resource efficiency logarithmic gain component reflecting the resource saving ratio. By performing a product operation on the above three nonlinear components, the normalized system robustness and cooperative gain coefficients are obtained.

[0125] The core logic of this formula in this implementation lies in the fact that it not only focuses on speed (the first term), but also implements a "veto system" through product operations. Even if the throughput is high, as long as the timing deviation in the second term ( If the value is too large, the exponential penalty term will be applied. It will approach 0, leading to the final A sharp decline. Meanwhile, the third term utilizes a logarithmic function. Resource conservation is encouraged, but with diminishing marginal returns, to prevent excessive performance sacrifices in the pursuit of resource saving. This coefficient provides an objective and comprehensive quantitative basis for subsequent parameter adjustments.

[0126] The steps in step S4, which involve updating the calculated weights of the timing urgency index using feedback compensation parameters and completing closed-loop timing calibration, specifically include: adopting a gradient-based adaptive update strategy to generate a feedback compensation parameter vector based on the calculated system robustness and cooperative gain coefficients.

[0127] The system utilizes the calculated system robustness and cooperative gain coefficient ( Using ) as the objective function, the following gradient adaptive update law is used to generate the feedback compensation parameter vector ( ):

[0128] ;

[0129] in This is the weight vector. The formula utilizes... As an error driving signal Based on the learning rate, This represents the annealing temperature coefficient.

[0130] The weight coefficients of each dimension used to calculate the temporal urgency index in step S1 are incrementally corrected using the feedback compensation parameter vector. The weight coefficients include the entropy weight coefficient corresponding to the generalized entropy term of the local gradient, the impact weight coefficient corresponding to the nonlinear instantaneous impact term, and the aging weight coefficient corresponding to the time-starvation aging factor term.

[0131] The system directly applies the generated feedback compensation parameter vector to step S1 to correct the three core weights in real time:

[0132] 1. Entropy weight coefficient ( ): The sensitivity of the control system to signal complexity.

[0133] 2. Impact weighting coefficient ( ): The level of attention the control system pays to sudden abnormal amplitudes.

[0134] 3. Aging weighting coefficient ( ): The forced refresh frequency of the low-priority channel by the control system.

[0135] During the correction process, if the system robustness and synergistic gain coefficient are in the high range, a small steady-state fine-tuning is performed; if the system robustness and synergistic gain coefficient are in the low range, a large parameter reshaping is performed, thereby enabling the system to adaptively evolve in response to changes in the industrial environment.

[0136] Through the exponential adjustment term in the above formula The system implements adaptive step size control: when When the value is close to 1 (high value range), it indicates that the system is operating well, the update step size approaches zero, and only minor steady-state fine-tuning is performed to resist disturbances; when At lower values ​​(low range), the exponential term becomes larger, resulting in a significant increase in the update step size. This prompts the system to drastically reshape its parameters (such as significantly reducing the aging weight to sacrifice fairness for high-frequency response), thereby quickly escaping the performance trap and achieving adaptive evolution to complex and ever-changing industrial environments.

[0137] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A multi-level timing optimization logic method for FPGA high-frequency data acquisition and processing, characterized in that, Includes the following steps: Step S1: Acquire multi-channel sensor signals through the FPGA parallel input interface, calculate the signal change rate and information entropy within the local time window of the multi-channel sensor signals, generate a timing urgency index representing the processing priority of each channel based on the calculation results, divide the multi-channel sensor signals into different processing levels according to the timing urgency index, and output a weighted data stream vector containing priority identifiers. Step S2: Receive the weighted data stream vector output from step S1, predict the timing conflict risk of the critical path based on the timing urgency index, and search for the global optimal solution in the 3D logic resource grid of the FPGA using the timing-resource coupling cost function. The timing-resource coupling cost function is used to quantify the correlation between logic delay, routing delay and resource contention heat, thereby generating the optimal logic layout configuration bit stream that guides the hardware resource connection relationship and the timing constraint boundary set that defines the timing safety range. Step S3: Load the optimal logic layout configuration bit stream generated in step S2, dynamically connect and reconstruct the configurable logic blocks and digital signal processing units inside the FPGA, and automatically insert pipeline registers in logic paths with insufficient setup time margins based on the timing prediction results in the optimal logic layout configuration bit stream, perform data feature extraction operations, and output execution performance indicators. Step S4: Compare the execution performance index output in step S3 with the timing constraint boundary set generated in step S2. When the execution performance index is detected to deviate from the timing constraint boundary set, calculate the feedback compensation parameter and transmit it back to step S1. Use the feedback compensation parameter to update the calculation weight of the timing urgency index to complete the closed-loop timing calibration.

2. The multi-level timing optimization logic method for FPGA high-frequency data acquisition and processing according to claim 1, characterized in that, The steps in step S1 for calculating the signal change rate of the multi-channel sensor signal and the information entropy within the local time window specifically include: The configurable input / output module of the field-programmable gate array receives differential signals from multiple channels, uses an internal hardware differentiator to calculate the first-order and second-order differences of the sampled values ​​in the current clock cycle, and assembles the sampled values, first-order differences, and second-order differences into a local state vector. Subsequently, the embedded digital signal processing slice performs gradient-based spatiotemporal entropy evolution operation on the local state vector. This operation quantifies the complexity of the signal waveform by analyzing the sparsity of the distribution of the second-order difference within the local time window, thereby replacing the statistical variance calculation and capturing the nonlinear abrupt changes in the signal.

3. The multi-level timing optimization logic method for FPGA high-frequency data acquisition and processing according to claim 2, characterized in that, Step S1, which generates a timing urgency index representing the processing priority of each channel based on the calculation results and outputs a weighted data stream vector containing priority identifiers, specifically includes: The timing urgency index is calculated using nonlinear weighted fusion logic, which consists of three dimensions: a local gradient generalized entropy term derived from the signal change rate to characterize the intensity of signal evolution; a nonlinear instantaneous impact term derived from the degree of deviation of the sampling point from the local median to characterize abnormal amplitude jumps; and a time starvation aging factor term that grows exponentially with the channel service waiting time to prevent data deadlock. After obtaining the timing urgency index, it is compared with a preset set of dynamic thresholds to determine the processing level state of the signal, and the label of this state is encapsulated into the original data frame header to construct a weighted data stream vector for subsequent steps.

4. The multi-level timing optimization logic method for FPGA high-frequency data acquisition and processing according to claim 3, characterized in that, Step S2, which involves predicting the temporal conflict risk of the critical path based on the temporal urgency index and constructing a 3D resource mesh and virtual potential field, specifically includes: Read the physical layout data of the field-programmable gate array and establish the mapping relationship between physical logic units and nodes in a three-dimensional discrete coordinate system; Based on the temporal urgency index output in step S1, a virtual potential field is constructed in a three-dimensional discrete coordinate system. Logical nodes associated with tasks with high temporal urgency index are defined as high gravitational potential wells with adsorption effects, so as to forcibly guide the surrounding physical interconnection resources to gather towards the node. At the same time, logical nodes associated with tasks with low temporal urgency index are defined as low potential energy regions, thereby pre-setting a topology structure with the shortest Manhattan distance for the critical path at the physical layout level.

5. The multi-level timing optimization logic method for FPGA high-frequency data acquisition and processing according to claim 4, characterized in that, Step S2, which uses the timing-resource coupling cost function to search for the global optimum in the FPGA's three-dimensional logic resource grid, specifically includes: A time-resource coupling cost function with three evaluation dimensions is constructed, and the minimum value of the function is solved using an iterative optimization algorithm; The three evaluation dimensions are: the urgency-weighted delay rate dimension, which uses the temporal urgency index as a weight to nonlinearly penalize the total physical path delay; the resource topology space dispersion dimension, which is used to quantify and constrain the degree of dispersion of logical units in physical space; and the dynamic spatiotemporal conflict overlap integral dimension, which is calculated by accumulating the conflict intensity of multiple tasks occupying the same resource in the continuous time domain. Finally, the optimal logical layout configuration bitstream is generated based on the globally optimal resource allocation scheme found, and the set of timing constraint boundaries is derived in reverse.

6. The multi-level timing optimization logic method for FPGA high-frequency data acquisition and processing according to claim 5, characterized in that, Step S3, which involves loading the optimal logic layout configuration bitstream and dynamically reconstructing the configurable logic blocks and digital signal processing units within the FPGA, specifically includes: The system calls the dynamic reconfigurable interface of the field-programmable gate array or the preset multiplexer network, and, while maintaining the continuity of the global clock signal, parses the routing instructions in the optimal logic layout configuration bit stream, and switches the state of the input and output routing switch matrix of the configurable logic block and digital signal processing slice. Simultaneously, currently idle computing resources are retrieved and dynamically allocated to data processing channels with high time urgency indices to construct dedicated parallel computing arrays.

7. The multi-level timing optimization logic method for FPGA high-frequency data acquisition and processing according to claim 6, characterized in that, Step S3, which involves configuring the timing prediction results in the bit stream based on the optimal logic layout and automatically inserting pipeline registers into logic paths with insufficient setup time margins, specifically includes: An online timing detection mechanism is activated to monitor the signal propagation delay of critical logic paths in real time. When the establishment time margin of a certain logic path is detected to be lower than the preset safety threshold, an elastic pipeline deep modulation strategy is executed. This strategy determines the optimal number of registers to be inserted by solving the extrema of a discrete optimization function. The discrete optimization function consists of a delay cost penalty term weighted by the timing urgency index and a timing robustness gain term calculated based on the setup time margin loss and the variance of the ambient clock jitter. Finally, based on the calculated optimal number of registers to be inserted, the pre-embedded shadow registers are activated at the key nodes of the combinational logic chain to cut the long combinational logic path into multiple short paths.

8. The multi-level timing optimization logic method for FPGA high-frequency data acquisition and processing according to claim 7, characterized in that, Step S4 compares the execution performance metrics output in step S3 with the time constraint boundary set generated in step S2. The specific steps involved when the execution performance metrics deviate from the time constraint boundary set include: The execution time-difference diagnostic logic calculates the difference between the actual path delay vector and instantaneous data throughput contained in the execution performance indicators and the time-constraint boundary set used as the benchmark reference system, and generates a time-difference tensor to characterize the degree of deviation. Simultaneously, the on-chip temperature sensor or flip-flop rate monitor is invoked to physically detect logic hotspots. If the value in the timing deviation tensor exceeds the preset tolerance threshold or the flip-flop rate of the logic region is abnormal, a switching instruction is immediately generated to activate the redundant logic path reserved in step S2 for seamless takeover, and the timing deviation tensor is marked as a parameter mismatch signal and transmitted to the subsequent calculation stage.

9. The multi-level timing optimization logic method for FPGA high-frequency data acquisition and processing according to claim 8, characterized in that, The steps in step S4 for calculating the feedback compensation parameters specifically include: A comprehensive evaluation model based on the coupling of the generalized logistic function and the exponential penalty term is constructed. This model is used to calculate the system robustness and cooperative gain coefficients used to quantify the effectiveness of the current system parameter configuration. The comprehensive evaluation model consists of three nonlinear components: The throughput saturation gain component is calculated using the ratio of actual throughput to baseline throughput; the timing deviation penalty component is calculated based on the maximum absolute norm of the timing deviation tensor; and the resource efficiency logarithmic gain component reflects the resource saving ratio. By performing a product operation on the above three nonlinear components, the normalized system robustness and cooperative gain coefficients are obtained.

10. A multi-level timing optimization logic method for FPGA high-frequency data acquisition and processing according to claim 9, characterized in that, Step S4, which involves updating the calculation weights of the timing urgency index using feedback compensation parameters and completing the closed-loop timing calibration, specifically includes: A gradient-based adaptive update strategy is adopted to generate a feedback compensation parameter vector based on the calculated system robustness and cooperative gain coefficient. The weight coefficients of each dimension used to calculate the time urgency index in step S1 are incrementally corrected using the feedback compensation parameter vector. The weight coefficients include the entropy weight coefficient corresponding to the generalized entropy term of the local gradient, the impact weight coefficient corresponding to the nonlinear instantaneous impact term, and the aging weight coefficient corresponding to the time starvation aging factor term. During the correction process, if the system robustness and cooperative gain coefficient are in the high range, a small steady-state fine-tuning is performed. If the system robustness and synergistic gain coefficient are in the low range, a significant parameter reshaping is performed to enable the system to adaptively evolve in response to changes in the industrial environment.

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