Intelligent control system of DDR6 memory structure of BGA central processing unit on desktop computer motherboard

Abstract

The application relates to the technical field of computer hardware, and particularly discloses an intelligent control system of a DDR6 memory structure of a desktop computer mainboard on-board BGA central processor, which comprises a hardware perception layer, a behavior modeling layer, a collaborative decision-making layer and a dynamic execution layer, realizes intelligent collaborative control of the processor and the memory subsystem from physical electrical characteristics to logical access behavior through real-time perception of hardware states, construction of a digital twin model, collaborative optimization of prefetching and scheduling strategies and closed-loop execution feedback, and thereby improves memory access efficiency, reduces delay and enhances system stability. The system not only focuses on the access mode at the software level, but also quantifies physical layer constraints such as signal integrity and power supply noise into computable model parameters, so that subsequent optimization decisions are established on the basis of the digital twin reflecting the real hardware state, and the problem that traditional control logic is disconnected with the physical reality is overcome, thereby laying a data foundation for the unification of high performance and high stability.

Description

Technical Field

[0001] This invention belongs to the field of computer hardware technology, specifically relating to an intelligent control system for the DDR6 memory structure of a desktop computer motherboard's onboard BGA central processing unit. Background Technology

[0002] In the field of computer hardware technology, the motherboard, as the platform that houses core components such as the central processing unit (CPU), memory, and chipset, directly determines the stability and operational efficiency of the entire computer system through its design and performance. Among these, the data interaction between the CPU and the memory subsystem is one of the key bottlenecks affecting system performance, and how to efficiently and intelligently manage this data path is a key area of ​​ongoing research in the industry.

[0003] The physical structure and electrical connections between the onboard BGA-packaged central processing unit and the DDR6 memory module constitute a highly integrated subsystem. The core objective of this subsystem is to achieve high-speed, low-latency access to memory data by the processor to meet the growing demands of compute-intensive applications. However, in existing technologies, the control logic of this subsystem primarily relies on static or semi-static prefetch strategies and fixed access paths.

[0004] Prefetching algorithms based on fixed patterns or simple historical access records are ill-suited for handling complex and variable workloads, resulting in low prefetch accuracy, a large amount of invalid data transfer, wasted memory bandwidth, and increased power consumption. Furthermore, memory controller scheduling strategies lack a deep understanding of the spatiotemporal correlations of data access, failing to dynamically optimize access sequences based on real-time task characteristics. In addition, physical layout constraints between the processor and memory, as well as signal integrity requirements, make the electrical characteristics of the data path a significant factor affecting access latency and stability; traditional control systems fail to coordinate the optimization of these physical layer constraints with logical layer scheduling. These problems are particularly pronounced when running applications with irregular data access patterns or high real-time requirements, limiting the full potential of the overall system performance. Summary of the Invention

[0005] The purpose of this invention is to provide an intelligent control system for the DDR6 memory structure of the onboard BGA central processing unit of a desktop computer motherboard, so as to solve the technical contradictions in the prior art caused by the static control logic, rigid prefetch strategy, and disconnect between physical constraints and logical scheduling, resulting in low memory bandwidth utilization, high access latency, power waste and limited system performance.

[0006] This invention provides an intelligent control system for a desktop computer motherboard with an onboard BGA-packaged central processing unit and a DDR6 memory structure. The system is deployed on the motherboard and electrically connected to the onboard BGA-packaged central processing unit and DDR6 memory modules, forming a collaborative closed-loop control system. The system includes a hardware perception layer, a behavior modeling layer, a collaborative decision-making layer, and a dynamic execution layer.

[0007] The hardware awareness layer is used to collect and quantify multi-dimensional state information of the processor and memory subsystem in real time. This layer integrates a physical signal monitoring module and a logic state capture module. The physical signal monitoring module collects, in real time, signal integrity parameters, power integrity parameters, and timing margin parameters of the data bus, including but not limited to those of the data bus, through a high-precision sensor network deployed on the critical signal paths between the processor and the memory controller. The signal integrity parameters specifically cover signal rise time, fall time, overshoot amplitude, and ringing attenuation coefficient. The power integrity parameters specifically cover core power supply voltage ripple amplitude and ground bounce noise level. The timing margin parameters are obtained by measuring the setup time and hold time window between the clock signal and the data signal. The logic state capture module captures, in real time, the instruction pointer trajectory of each processor core, cache hit rate sequence, memory access request queue depth, activation and precharge status of each DDR6 memory bank, line buffer hit history, and memory access latency distribution histogram through the processor's built-in performance monitoring unit and the memory controller's status register.

[0008] The behavior modeling layer is used to construct and continuously update a digital twin model representing the dynamic behavior of the system based on real-time multi-dimensional state information collected by the hardware perception layer. This digital twin model consists of a coupled physical constraint sub-model and an access pattern sub-model. The physical constraint sub-model maps the physical signal parameters collected by the hardware perception layer into a time-varying electrical performance scoring matrix. Each element of this matrix corresponds to the signal quality score of a key signal path under a specific frequency and load. A score below a preset threshold indicates a signal integrity risk for that path. The access pattern sub-model uses a bidirectional long short-term memory network based on an attention mechanism to model the access sequences provided by the logic state capture module. This network takes the processor instruction pointer trajectory and memory access address sequence as input, learns long-distance spatiotemporal dependencies through its internal gating mechanism, and outputs a joint probability prediction distribution for subsequent memory access addresses, access types, and access time intervals.

[0009] The collaborative decision-making layer receives the digital twin model output from the behavioral modeling layer and generates a globally optimal memory control strategy based on it. This layer includes a prefetch strategy optimization module and an access path scheduling module. The prefetch strategy optimization module first calculates the expected utility value of each memory address accessed within a future time window based on the joint probability prediction distribution output by the access pattern sub-model. The expected utility value is calculated by weighting the access probability with the inverse of the predicted access latency. Subsequently, this module marks addresses with expected utility values ​​higher than a dynamic threshold as high-value prefetch candidates. The dynamic threshold is adaptively adjusted based on the current memory bandwidth utilization and processor cache miss rate. The access path scheduling module synchronously receives the electrical performance score matrix output by the physical constraint sub-model and the high-value prefetch candidate list generated by the prefetch strategy optimization module. This module incorporates a multi-objective optimization solver. The solver's objective function aims to minimize the overall access latency while maximizing the reliability score of the signal path and satisfying the protocol timing constraints of the memory controller and the DDR6 memory module. The solver iteratively searches for each memory access request, including real-time requests and prefetch requests, allocating specific physical access channels and scheduling timings, and generating a scheduling instruction set containing access command sequences, target memory, row addresses, column addresses, and precise clock cycle offsets.

[0010] The dynamic execution layer receives the scheduling instruction set generated by the collaborative decision-making layer and transforms it into precise hardware control signals to drive the memory controller and related physical interfaces. This layer includes an instruction compilation module and a real-time feedback module. The instruction compilation module compiles the scheduling instruction set into specific command words conforming to the DDR6 memory protocol specification and injects them into the memory controller's command queue. Simultaneously, this module fine-tunes the output phase of the phase-locked loop based on the clock cycle offset in the scheduling instruction set to achieve nanosecond-level precise control of access timing. The real-time feedback module monitors the actual effects after instruction execution, including actual access latency, command execution success rate, and the latest round of status information collected by the hardware perception layer. This execution feedback information is sent back to the hardware perception layer and the behavior modeling layer in real time to trigger immediate updates to the digital twin model and subsequent rolling optimization of control strategies.

[0011] As one embodiment of the present invention, the attention weight calculation process in the bidirectional long short-term memory network of the attention mechanism is as follows: For the feature vector of each time step in the input sequence, the network first calculates its correlation score with the feature vectors of all time steps in the sequence; the correlation score is calculated by a learnable weight matrix and a nonlinear activation function; then, all correlation scores are normalized to obtain the attention weight distribution; finally, the context vector of the current time step is generated by weighted summation of the feature vectors of all time steps in the sequence according to their attention weights; after concatenating the context vector with the original feature vector of the current time step, it is sent to the bidirectional long short-term memory network unit for further processing.

[0012] Furthermore, the optimization objective function adopted by the multi-objective optimization solver is specifically described as follows: minimizing the total cost function; the total cost function is composed of a linear weighted sum of delay cost, risk cost, and conflict cost; the delay cost is the sum of the predicted delays of all scheduled access requests; the risk cost is the sum of the penalty values ​​of all allocated signal paths whose electrical performance scores are lower than the safety threshold, and the penalty value is proportional to the degree to which the score is lower than the threshold; the conflict cost is used to penalize scheduling schemes that violate DDR6 memory protocol timing rules, such as row activation to precharge time and column address gating delay, and its value is the product of the number of rule violations and a fixed penalty coefficient; the solver performs an iterative search in the solution space using a simulated annealing algorithm to find the scheduling scheme that minimizes the total cost function.

[0013] Furthermore, the high-precision sensor network includes a differential probe array and a voltage noise sampling unit; the differential probe array is non-invasively coupled to the vicinity of key high-speed differential signal pairs on the motherboard via microstrip line coupling to sense differential signal waveforms; the voltage noise sampling unit is directly connected to decoupling capacitor measurement points near the processor core power supply pins and memory power supply pins via a high-bandwidth operational amplifier and an analog-to-digital converter to sample power supply noise.

[0014] As one embodiment of the present invention, the update triggering mechanism of the digital twin model is based on an incremental learning framework. When the deviation between the actual access delay monitored by the real-time feedback module and the delay predicted by the collaborative decision-making layer continues to exceed the preset tolerance range for three consecutive cycles, or when the hardware perception layer detects a sudden change in the electrical performance score of any signal path with a change amplitude exceeding 20%, the system immediately triggers the behavior modeling layer to fine-tune the parameters of the access pattern sub-model or the physical constraint sub-model. The parameter fine-tuning adopts a gradient descent algorithm based on time difference, with the goal of minimizing the mean square error between the predicted value and the actual observed value.

[0015] Furthermore, the real-time feedback module of the dynamic execution layer also integrates an error recovery submodule. When a memory access command execution failure or verification error is detected, the error recovery submodule is immediately activated. It first freezes the current command queue, and then retrieves the pre-stored recovery strategy library according to the error type. The recovery strategy library contains standardized recovery procedures for different error codes, such as performing a refresh operation on a specific memory bank, recalibrating the drive strength and equalization parameters of the relevant signal path, or falling back to a conservative but stable access timing configuration. After the recovery process is completed, the system re-injects scheduling instructions from the frozen point and marks the relevant path as a temporary high-risk state for the collaborative decision-making layer to avoid in subsequent scheduling.

[0016] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0017] 1. This invention, by constructing a hardware perception layer and a behavior modeling layer, achieves for the first time in a motherboard-level control system a comprehensive, high-precision, real-time digital modeling of the processor and memory subsystem, from physical electrical characteristics to logical access behavior. This system not only focuses on software-level access patterns but also quantifies physical constraints such as signal integrity and power supply noise into computable model parameters. This allows subsequent optimization decisions to be based on a digital twin reflecting the true hardware state, fundamentally overcoming the problem of traditional control logic being disconnected from physical reality and laying a data foundation for the unification of high performance and high stability.

[0018] 2. This invention integrates prefetch optimization and access path scheduling within a unified multi-objective optimization framework through a collaborative decision-making layer. The prefetch strategy optimization module uses a deep time-series model for accurate prediction, initiating prefetching only for high-value data, significantly improving prefetch accuracy and memory bandwidth utilization. The access path scheduling module simultaneously considers electrical risks and protocol constraints, dynamically selecting the optimal physical channel and timing for each access, effectively reducing access failures and latency jitter caused by signal degradation. This hardware-software collaborative, logical-physical linkage optimization mechanism can adapt to complex and variable workloads, significantly reducing overall access latency and improving system stability under extreme loads.

[0019] 3. This invention constructs a complete closed-loop control system through a dynamic execution layer and a real-time feedback module. The system can provide immediate feedback based on the actual effect of command execution and trigger online learning of the model and rolling optimization of the strategy. This intelligent control closed loop of self-perception, self-decision-making, self-execution, and self-correction enables the system to continuously evolve and adapt to environmental changes. In particular, the integrated error recovery submodule can quickly implement targeted recovery when hardware anomalies occur, greatly enhancing the system's fault tolerance and reliability, and ensuring the continuous and stable execution of critical computing tasks. Attached Figure Description

[0020] Figure 1 This is a schematic diagram of the overall technical solution architecture of the present invention;

[0021] Figure 2 This is a schematic diagram of the core principle framework of behavior modeling and collaborative decision-making based on digital twin models in this invention;

[0022] Figure 3 This is a flowchart illustrating the logical flow of the hardware perception layer in this invention for acquiring multi-dimensional state information.

[0023] Figure 4 This is a schematic diagram of the multi-objective collaborative decision-making framework for prefetch optimization and access path scheduling in the collaborative decision-making layer of this invention;

[0024] Figure 5 This is a schematic diagram of the closed-loop control and adaptive update framework consisting of dynamic execution and real-time feedback in this invention. Detailed Implementation

[0025] Please refer to the attached document. Figures 1 to 5 The intelligent control system for the DDR6 memory structure of the onboard BGA central processing unit on a desktop computer motherboard, as described in this invention, is a closed-loop intelligent control system deployed on the desktop computer motherboard and electrically connected to the onboard BGA-packaged central processing unit and DDR6 memory module. This system, through a four-layer architecture of hardware perception layer, behavior modeling layer, collaborative decision-making layer, and dynamic execution layer, achieves full-dimensional perception, modeling, optimization, and execution of the processor and memory subsystem from physical signals to logical behaviors. Please refer to the appendix. Figure 1 The figure is a schematic diagram of the overall technical solution architecture of the present invention, which clearly shows the data flow and control closed loop relationship between the four-layer modules.

[0026] In the hardware perception layer, the system uses a physical signal monitoring module and a logic state capture module to perform high-precision, multi-dimensional real-time acquisition of the operating status of the processor and memory subsystem. The physical signal monitoring module consists of a differential probe array and a voltage noise sampling unit. The differential probe array is deployed non-invasively on the motherboard near all critical high-speed differential signal pairs between the processor and the DDR6 memory controller using microstrip line coupling. It is used to sense differential signal waveforms and extract signal integrity parameters. These parameters include signal rise time, fall time, overshoot amplitude, and ringing attenuation coefficient. Its sampling frequency is no less than 20 GHz to ensure accurate capture of nanosecond-level signal transient characteristics.

[0027] The voltage noise sampling unit is directly connected to the decoupling capacitor measurement points near the processor core power supply pins and DDR6 memory power supply pins via a high-bandwidth operational amplifier and a 16-bit analog-to-digital converter. This allows for continuous sampling of power integrity parameters, including core power supply voltage ripple amplitude and ground bounce noise level. The sampling rate is set to 1 MHz, with a resolution of 1 millivolt. Furthermore, timing margin parameters are measured in real-time by a dedicated timing analysis circuit, measuring the setup and hold times between the clock and data signals. A warning signal is triggered when the window width falls below a preset safety threshold (e.g., 50 picoseconds).

[0028] The logic state capture module relies on the processor's built-in performance monitoring unit and the memory controller's status register to poll and record the logic-level operating status every 100 nanoseconds. The captured data includes: the current instruction pointer trajectory for each processor core, L1 / L2 / L3 cache hit rate sequences, memory access request queue depth, activation and precharge status of each DDR6 memory bank, line buffer hit history, and a memory access latency distribution histogram. Specifically, the instruction pointer trajectory is recorded in 64-bit virtual address format, the cache hit rate is expressed as a percentage, the queue depth is an integer count, the memory bank status is encoded using a binary bitmap (1 for active, 0 for idle), the line buffer hit history is stored as a Boolean sequence of the last 128 accesses, and the latency distribution histogram is divided into 10 picosecond intervals, covering the range from 0 to 5 nanoseconds. All of the above data is packaged into fixed-length status frames and transmitted to the behavior modeling layer via a dedicated low-latency channel.

[0029] Please refer to the attached document. Figure 3 This diagram details the logical flow framework for the hardware perception layer to collect multi-dimensional state information. After data collection is complete, the state frame is sent to the behavior modeling layer to construct and continuously update a digital twin model representing the dynamic behavior of the system. This digital twin model consists of a coupled physical constraint sub-model and an access pattern sub-model, both sharing the same timestamp reference to ensure model synchronization.

[0030] The physical constraint sub-model receives raw signal parameters from the physical signal monitoring module and maps them to a time-varying electrical performance scoring matrix. This matrix has the following dimensions: × ,in This represents the number of critical signal paths (e.g., 32 data lines, 8 address lines, 4 clock lines, etc., totaling 44). This represents the load scenario category under the current operating frequency (e.g., light load, medium load, and heavy load). Each element P in the matrix... ) indicates the first The signal path is in the first Signal quality scoring under similar load conditions, ranging from 0 to 100 points. The score calculation is based on a pre-trained multilayer perceptron model, with inputs including seven physical parameters such as rise time, overshoot amplitude, and ripple amplitude, and outputting a comprehensive score. When P( When the score is below a preset threshold (e.g., 70 points), the path is marked as having a signal integrity risk, and subsequent scheduling needs to avoid it or apply additional drive compensation.

[0031] The access pattern sub-model employs a bidirectional long short-term memory network based on an attention mechanism to model the access sequences provided by the logic state capture module. This network uses the processor instruction pointer trajectory and memory access address sequence as dual-channel inputs. First, it maps the 64-bit virtual address to a 128-dimensional embedding vector using a hash function, then concatenates it with the instruction pointer embedding vector to form a 256-dimensional feature vector sequence. Internally, the network contains two LSTM branches, forward and backward, to handle the forward and reverse time dependencies, respectively. At each time step... The network calculates the current feature vector. With all historical feature vectors in the sequence correlation score ,in For learnable weight matrix, For the bias term, [·;·] denotes vector concatenation. Then, for... Perform Softmax normalization to obtain the attention weights. Final context vector With the original The data is concatenated and fed into an LSTM unit. The network output is for the future. time steps (e.g.) The model calculates the probability distribution of memory addresses accessed within memory address range 8, the probability of access type (read / write), and the expected value of the access time interval. The weights are updated every 500 nanoseconds to adapt to workload changes.

[0032] Please refer to the attached document. Figure 2 This diagram illustrates the core principles and framework of behavioral modeling and collaborative decision-making. After the digital twin model outputs, it is fed into the collaborative decision-making layer. This layer contains a prefetch strategy optimization module and an access path scheduling module, which work together to generate a globally optimal memory control strategy.

[0033] The prefetching strategy optimization module first parses the joint probability prediction distribution output by the access pattern sub-model. For each predicted address... Calculate its expected utility value ,in For address The probability of being accessed within a future window. To predict access latency, This is an adjustable weighting coefficient, with a default value of 0.6. This formula reflects the dual value orientation of high probability and low latency. Subsequently, the module sets a dynamic threshold. , The initial value is 0.5, but it will be adjusted based on the current memory bandwidth utilization. processor cache miss rate Perform adaptive adjustments: .all Addresses are marked as high-value prefetch candidates and added to the prefetch candidate list. This list has a maximum capacity of 64 items and is managed using a priority queue, with priorities determined by... Sort in descending order.

[0034] The access path scheduling module synchronously receives the electrical performance score matrix output by the physical constraint sub-model and the high-value prefetch candidate list generated by the prefetch strategy optimization module. This module has a built-in multi-objective optimization solver whose optimization objective is to minimize the total cost function. , It consists of three linearly weighted terms:

[0035]

[0036] in, This is the sum of the predicted latency for all pending access requests (including real-time requests and prefetch requests); The penalty value is the sum of the penalty values ​​for all assigned signal paths whose electrical performance scores are below the safety threshold. ; This is a penalty for violating the timing rules of the DDR6 memory protocol. For example, if the row activation to precharge time t_RAS of a memory bank in the scheduling scheme is less than the specification requirement of 35 nanoseconds, then each violation is counted as one violation. Weighting coefficients , , The default values ​​are 0.7, 0.2, and 0.1, respectively, and can be dynamically adjusted according to system stability requirements.

[0037] The solver employs simulated annealing to iteratively search the solution space. The initial solution is generated using a greedy strategy: prioritizing the allocation of high-scoring paths and scheduling high-priority requests. In each iteration, the current solution is randomly perturbed (e.g., swapping the channel allocation of two requests, fine-tuning the timing offset by ±1 clock cycle), and the new solution is calculated. Value. If If the value is less than 0, then accept the new solution; otherwise, use probability. Accept, among which The current temperature is initially set to 100, decreasing by 5% every 100 iterations. The search continues until the temperature drops below 1 or there is no improvement after 500 consecutive iterations. The final output is a set of scheduling instructions, containing the command type (ACT / PRE / READ / WRITE), target memory number, row address, column address, target physical channel number, and precise clock cycle offset relative to the current clock edge (unit: clock cycle, precision 0.25 cycles).

[0038] Please refer to the attached document. Figure 4 This diagram illustrates a multi-objective collaborative decision-making framework for prefetch optimization and access path scheduling in the collaborative decision-making layer. After the scheduling instruction set is generated, it is sent to the dynamic execution layer.

[0039] The dynamic execution layer comprises an instruction compilation module and a real-time feedback module. The instruction compilation module first parses each instruction in the scheduling instruction set into a specific command word conforming to the JEDEC DDR6 protocol specification. For example, the READ command is compiled into a valid combination of signals such as CAS, RAS, WE, and CS, with additional fields for the target memory bank, the high-order bits of the row address, and the column address. The command word is injected into the memory controller's command queue, which has a depth of 256 entries and is managed using a first-in, first-out (FIFO) strategy, but supports high-priority instruction insertion. Simultaneously, the instruction compilation module sends phase fine-tuning instructions to the phase-locked loop (PLL) based on the clock cycle offset in the scheduling instruction. The PLL has a built-in digitally controlled delay line, enabling phase adjustments at the 0.1 nanosecond level to ensure precise alignment of the data sampling window with the signal eye diagram center.

[0040] The real-time feedback module continuously monitors the actual effect after command execution. It obtains the actual access latency and command execution success rate (number of successes / total number of successes) through the memory controller's performance counters, and acquires the latest round of physical and logical state information through the hardware awareness layer. Feedback data is packaged into feedback frames and sent every 200 nanoseconds. When the absolute deviation between the actual access latency and the predicted latency is detected to exceed the preset tolerance for three consecutive cycles, or when the electrical performance score of any signal path changes by more than 20% (e.g., from 85 points to 60 points), the system immediately triggers the incremental learning mechanism of the behavior modeling layer.

[0041] Incremental learning employs a gradient descent algorithm based on temporal differences. For the access pattern sub-model, the loss function is defined as the cross-entropy between the predicted address and the actual access address; for the physical constraint sub-model, the loss function is the mean squared error between the predicted score and the measured score. The learning rate is initially 0.01 and decays exponentially with the duration of the bias. Model parameters are updated only under triggered conditions to avoid system oscillations caused by frequent perturbations.

[0042] In addition, the real-time feedback module integrates an error recovery submodule. When the memory controller reports command execution failure (e.g., CRC checksum error, command timeout) or the hardware awareness layer detects severe signal degradation (e.g., ringing attenuation coefficient below 0.3), the error recovery submodule immediately activates. It first sends a freeze command to the memory controller, suspending the dequeue operation of the command queue. Then, it retrieves a pre-stored recovery strategy library based on the error code. The strategy library is stored in key-value pairs, where the key is the error type (e.g., DQ signal CRC error, VDDQ voltage drop), and the value is a standardized recovery procedure. For example, for a row address conflict error in a specific memory bank, the procedure includes: executing a REFRESH command on the memory bank, disabling its row buffer, and reactivating it after waiting 200 nanoseconds; for excessive signal overshoot, the procedure includes: reducing the slew rate of the driver for that signal path, enabling the on-chip equalizer, and increasing the termination resistor value. After the recovery procedure is completed, the system unfreezes, re-injects scheduling instructions from the original freeze point, and marks the relevant path as a temporary high-risk state in the physical constraint sub-model (with a forced score of 50), allowing the collaborative decision-making layer to avoid it within the next 10 milliseconds.

[0043] Please refer to the attached document. Figure 5 The diagram illustrates a closed-loop control and adaptive update framework comprised of dynamic execution and real-time feedback. The entire system operates in a 500-nanosecond cycle: hardware perception → behavior modeling → collaborative decision-making → dynamic execution → real-time feedback → model update. Within this closed loop, the system not only responds to the current load but also predicts future behavior, mitigates physical risks, and corrects execution deviations, achieving true intelligent control.

[0044] In typical application scenarios, when a system runs large scientific computing programs, the processor generates a large number of regular memory accesses. The access pattern sub-model of the behavior modeling layer quickly identifies the periodicity of the address sequences and predicts the subsequent access locations with high confidence. Based on this, the prefetch strategy optimization module generates high-value prefetch candidates, and the access path scheduling module allocates them to channels with the best electrical performance (e.g., data lines far away from high-frequency interference sources) and precisely aligns the timing windows.

[0045] In summary, this embodiment, through a four-layer closed-loop architecture, deeply integrates physical layer perception, logical layer modeling, global optimization, and precise execution, solving the fundamental defects of traditional memory control systems that are static, rigid, and disconnected, and achieving a unity of high performance, high energy efficiency, and high reliability.

[0046] Based on the aforementioned embodiments, this embodiment further extends the coupling mechanism of the digital twin model and the solution strategy of the multi-objective optimization solver to further improve the robustness and adaptability of the system under extreme conditions.

[0047] In the behavioral modeling layer, the coupling between the physical constraint sub-model and the access pattern sub-model is no longer limited to simple data transfer, but introduces a cross-modal attention mechanism. Specifically, when predicting future access addresses, the access pattern sub-model actively queries the current scores of the signal paths associated with the corresponding addresses in the physical constraint sub-model. For example, if the predicted address 'a' maps to data line DQ[15:0] and address line A[10:0], the model will extract the average scores of these paths. .like If the score is less than 75, a decay factor is applied when calculating the access probability of that address. This mechanism embeds physical feasibility constraints into the access pattern prediction itself, avoiding the generation of high-probability but unreliable prefetch suggestions.

[0048] Furthermore, the digital twin model introduces an environmental state embedding vector. This vector is provided by motherboard environmental sensors, including ambient temperature, chassis fan speed, motherboard tilt angle, etc. These parameters are mapped to a 32-dimensional vector through the embedding layer and injected as additional input into the initial hidden state of the bidirectional LSTM network.

[0049] At the collaborative decision-making layer, the multi-objective optimization solver employs a hierarchical solution strategy. The first layer is a fast heuristic scheduler, used to handle high-priority real-time requests (such as service interruptions and real-time audio / video streams). Its decisions are based on local optima: selecting the currently idle channel with the highest score and assigning it the earliest feasible timing. The second layer is a global optimization scheduler, handling prefetch requests and low-priority background requests, running a complete simulated annealing algorithm. The two layers are synchronized through a shared resource state table to ensure no conflicts. This hierarchical mechanism keeps 95% of real-time request scheduling latency within 100 nanoseconds while ensuring that the depth of global optimization is not affected.

[0050] Furthermore, risk cost item The calculation incorporates a path aging factor. Each signal path maintains an aging counter, which increments each time it is used under high load (bandwidth utilization > 80%). A higher aging counter value results in a larger penalty value for the same score, reflecting the negative impact of long-term stress accumulation on reliability. This mechanism effectively extends the motherboard's lifespan under high loads.

[0051] At the dynamic execution layer, the instruction compilation module supports dynamic voltage and frequency scaling in conjunction with the control. When the collaborative decision-making layer detects a continuous low-load state, the instruction compilation module sends a request to the power management unit to reduce the operating frequency of the DDR6 memory from 6400 MHz to 4800 MHz, while simultaneously reducing the VDDQ voltage by 0.1 volts. During the frequency switching process, the scheduling instruction set automatically inserts necessary training sequences and timing recalibration commands to ensure a seamless transition.

[0052] The error recovery submodule of the real-time feedback module has been enhanced with self-learning capabilities. After each recovery strategy is executed, the system records the state characteristics before recovery (such as error type, signal score, and temperature) and the recovery effect (success / failure, recovery time). These records are used to update the recovery strategy library online: if a strategy fails three times consecutively, its priority is downgraded; if a new error type appears and there is no matching strategy, the expert system is triggered to generate a temporary strategy and submit it to the cloud learning platform. This mechanism enables the system to continuously evolve.

[0053] With the above enhancements, this embodiment demonstrates stronger stability and adaptability when facing challenges such as complex electromagnetic interference, long-term high-load operation, and sudden hardware aging, making it suitable for demanding application scenarios such as workstations, servers, and high-performance computing.

Claims

1. An intelligent control system for a desktop computer motherboard with an onboard BGA central processing unit and DDR6 memory structure, characterized in that, include: The hardware awareness layer is used to collect and quantify multi-dimensional state information of the processor and memory subsystem in real time. The behavior modeling layer is used to construct and continuously update a digital twin model representing the dynamic behavior of the system based on the real-time multidimensional state information collected by the hardware perception layer. The collaborative decision-making layer is used to receive the digital twin model output by the behavior modeling layer and generate a globally optimal memory control strategy accordingly. The digital twin model is composed of a coupled physical constraint sub-model and an access mode sub-model; The dynamic execution layer is used to receive the scheduling instruction set generated by the collaborative decision-making layer and convert the scheduling instruction set into precise hardware control signals to drive the memory controller to execute. The collaborative decision-making layer includes a prefetching strategy optimization module and an access path scheduling module; The prefetch strategy optimization module calculates the expected utility value of each memory address being accessed within the future time window based on the joint probability prediction distribution output by the access pattern sub-model, and marks addresses with expected utility values ​​higher than the dynamic threshold as high-value prefetch candidates. The access path scheduling module synchronously receives the electrical performance score matrix output by the physical constraint sub-model and the high-value prefetch candidate list generated by the prefetch strategy optimization module. Based on the built-in multi-objective optimization solver, it allocates a specific physical access channel and scheduling sequence for each memory access request, and generates a scheduling instruction set containing access command sequence, target memory, row address, column address and precise clock cycle offset. The dynamic execution layer includes an instruction compilation module and a real-time feedback module; The instruction compilation module compiles the scheduling instruction set into specific command words that conform to the DDR6 memory protocol specification and injects them into the command queue of the memory controller. At the same time, it fine-tunes the output phase of the phase-locked loop according to the clock cycle offset in the scheduling instruction set. The real-time feedback module monitors the actual effect after the command is executed and sends the execution feedback information, including the actual access latency, command execution success rate and new round of status information, back to the hardware perception layer and the behavior modeling layer in real time. The optimization objective function used by the multi-objective optimization solver is specifically described as: minimizing the total cost function; The total cost function is a linear weighted sum of the delay cost, risk cost, and conflict cost; the delay cost is the sum of the predicted delays of all scheduled access requests. The risk cost term is the sum of the penalty values ​​for all assigned signal paths with electrical performance scores below the safety threshold. The penalty value is proportional to the degree to which the score is below the threshold. The conflict cost term is used to penalize scheduling schemes that violate the timing rules of the DDR6 memory protocol. Its value is the product of the number of rule violations and a fixed penalty coefficient. The multi-objective optimization solver uses the simulated annealing algorithm to iteratively search the solution space to find the scheduling scheme that minimizes the total cost function; The update triggering mechanism of the digital twin model is based on an incremental learning framework; When the actual access delay monitored by the real-time feedback module deviates from the delay predicted by the collaborative decision-making layer for more than three consecutive cycles, or when the hardware perception layer detects a sudden change in the electrical performance score of any signal path with a change of more than 20%, the system immediately triggers the behavior modeling layer to fine-tune the parameters of the access mode sub-model or the physical constraint sub-model. The parameter fine-tuning employs a gradient descent algorithm based on time difference, with the goal of minimizing the mean square error between the predicted and actual observed values.

2. The intelligent control system for the DDR6 memory structure of the onboard BGA central processing unit of a desktop computer motherboard according to claim 1, characterized in that, The hardware sensing layer includes a physical signal monitoring module and a logic state capture module; The physical signal monitoring module collects signal integrity parameters, power integrity parameters, and timing margin parameters of the data bus in real time through a high-precision sensor network deployed on the critical signal path between the processor and the memory controller. The logic state capture module captures, in real time, the instruction pointer trajectory of each core of the processor, cache hit rate sequence, memory access request queue depth, activation and precharge status of each DDR6 memory bank, row buffer hit history, and memory access latency distribution histogram through the processor's built-in performance monitoring unit and the status register of the memory controller.

3. The intelligent control system for the DDR6 memory structure of the onboard BGA central processing unit on a desktop computer motherboard according to claim 2, characterized in that, The physical constraint sub-model maps the physical signal parameters collected by the hardware perception layer into a time-varying electrical performance scoring matrix. Each element of the electrical performance scoring matrix corresponds to the signal quality score of a key signal path under a specific frequency and load. The access pattern sub-model uses a bidirectional long short-term memory network based on an attention mechanism to model the access sequence provided by the logic state capture module. The bidirectional long short-term memory network based on an attention mechanism takes the processor instruction pointer trajectory and the memory access address sequence as input and outputs a joint probability prediction distribution of subsequent memory access addresses, access types and access time intervals.

4. The intelligent control system for the DDR6 memory structure of the onboard BGA central processing unit of a desktop computer motherboard according to claim 3, characterized in that, The high-precision sensor network includes a differential probe array and a voltage noise sampling unit; The differential probe array is non-invasively coupled to the vicinity of the critical high-speed differential signal line pairs on the motherboard using microstrip line coupling, and is used to sense differential signal waveforms; The voltage noise sampling unit is directly connected to the decoupling capacitor measurement points near the processor core power supply pin and memory power supply pin via a high-bandwidth operational amplifier and analog-to-digital converter, and is used to sample power supply noise.

5. The intelligent control system for the DDR6 memory structure of the onboard BGA central processing unit on a desktop computer motherboard according to claim 4, characterized in that, In the bidirectional long short-term memory network of the aforementioned attention mechanism, the calculation process of attention weights is as follows: For each feature vector at a time step in the input sequence, the network first calculates the relevance score with the feature vectors of all time steps in the sequence. The relevance score is calculated using a learnable weight matrix and a non-linear activation function. Then, all relevance scores are normalized to obtain the attention weight distribution. Finally, the context vector of the current time step is generated by weighting and summing the feature vectors of all time steps in the sequence according to the attention weights. This context vector is concatenated with the original feature vector of the current time step and then fed into a bidirectional long short-term memory network unit for further processing.

6. The intelligent control system for the DDR6 memory structure of the onboard BGA central processing unit of a desktop computer motherboard according to claim 5, characterized in that, The real-time feedback module also integrates an error recovery submodule; when a memory access command execution failure or verification error is detected, the error recovery submodule is immediately activated. The error recovery submodule first freezes the current command queue, and then retrieves the pre-stored recovery strategy library according to the error type; The recovery strategy library contains standardized recovery procedures for different error codes; After the recovery process is completed, the system re-injects scheduling instructions from the frozen point and marks the relevant paths as temporarily high-risk.

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