EDA software implementation method and system for memory IP layout optimization

Abstract

The invention provides an EDA software implementation method and system for memory IP layout optimization, and relates to the technical field of memorizers, the method comprises the following steps: receiving a memory IP design parameter and an optimization target input by a user, and calling a matched basic unit module from a pre-established hierarchical memory IP library according to the design parameter; an initial memory IP layout scheme is generated based on the basic unit module, the initial memory IP layout scheme is rapidly evaluated by utilizing a neural network model, a plurality of optimized layout candidate schemes are generated by adopting a genetic algorithm, and each optimized layout candidate scheme comprises different basic unit arrangements, metal layer use strategies and buffer insertion positions; performing parallel simulation on the optimal layout candidate scheme, and selecting an optimal layout scheme; and performing post-simulation verification on the optimal layout scheme, wherein the post-simulation verification comprises static time sequence analysis, dynamic power consumption analysis and electromagnetic compatibility analysis.

Description

Technical Field

[0001] The present invention relates to memory technology, and in particular to an EDA software implementation method and system for memory IP layout optimization. Background Art

[0002] As the complexity of integrated circuit design continues to increase, memory IP plays an increasingly important role in modern chip design. Memory IP not only occupies most of the chip area, but also directly affects the performance, power consumption and reliability of the chip. Therefore, the layout optimization of memory IP has become a key research direction in the field of electronic design automation (EDA).

[0003] Traditional memory IP layout methods mainly rely on designers' experience and manual adjustments, which is often inefficient and difficult to obtain the optimal solution when facing modern complex chip designs. With the development of EDA technology, some automated memory IP layout tools have begun to be used, but there are still the following problems:

[0004] Single optimization target: Existing EDA tools usually only focus on optimizing a single indicator, such as minimizing area or power consumption, and it is difficult to achieve comprehensive optimization of multiple targets. Poor scalability: With the increase in chip size and the increase in the number of memory IP types, the computational complexity of existing tools has increased exponentially, making it difficult to meet the needs of large-scale design.

[0005] Insufficient process adaptability: With the continuous evolution of semiconductor processes, the memory IP layout rules under new process nodes have become increasingly complex, and existing tools are difficult to quickly adapt to the requirements of new processes. Lack of intelligent decision-making capabilities: Most EDA tools still use traditional heuristic algorithms, lack the learning and accumulation of design patterns, and cannot use historical design experience to guide new layout optimization.

[0006] Insufficient cross-domain optimization capabilities: The collaborative optimization between memory IP layout and other design stages (such as logic synthesis, timing analysis, etc.) is weak, making it difficult to achieve global optimization. Poor user interactivity: Existing tools often operate in the form of "black boxes", making it difficult for designers to understand and intervene in the optimization process, and unable to fully utilize the advantages of human-computer collaboration. Insufficient consideration of reliability: At advanced process nodes, the reliability issues of memory IP (such as soft errors, hotspot management, etc.) are becoming increasingly prominent, but existing layout tools do not fully consider these issues.

[0007] In view of the above problems, the industry urgently needs a new type of memory IP layout optimization EDA software that can comprehensively consider multiple optimization goals, has good scalability and process adaptability, can intelligently learn and use historical design experience, achieve collaborative optimization with other design stages, and has good user interactivity. At the same time, the new software should also be able to fully consider advanced requirements such as reliability, accurate power consumption modeling, system-level optimization, and security to meet the complex needs of modern integrated circuit design. Summary of the invention

[0008] The embodiments of the present invention provide an EDA software implementation method and system for optimizing memory IP layout, which can solve the problems in the prior art.

[0009] According to a first aspect of the embodiments of the present invention,

[0010] Provides EDA software implementation methods for memory IP layout optimization, including:

[0011] receiving memory IP design parameters and optimization targets input by a user, wherein the design parameters include memory type, capacity, bit width, process node, voltage domain, clock domain, power consumption target, and area limit; calling a matching basic unit module from a pre-established hierarchical memory IP library according to the design parameters, wherein the hierarchical memory IP library includes memory units of different process nodes and performance levels;

[0012] Generate an initial memory IP layout scheme based on the basic unit module, including determining the memory array structure, peripheral circuit layout and metal layer allocation; use a neural network model to quickly evaluate the initial memory IP layout scheme, and use a genetic algorithm to generate multiple optimized layout candidate schemes, each of which includes a different basic unit arrangement, metal layer usage strategy and buffer insertion position; perform parallel simulation on the optimized layout candidate schemes to select the optimal layout scheme; perform post-simulation verification on the optimal layout scheme, including static timing analysis, dynamic power consumption analysis and electromagnetic compatibility analysis;

[0013] If the verification result does not meet the design goal, the adaptive optimization process is started: using the deep reinforcement learning algorithm, the optimization strategy and weights are dynamically adjusted according to the historical optimization data and the current verification results; the optimization plan is regenerated based on the adjusted strategy, and the parallel simulation steps are returned to execute until the optimization goal is met or the preset number of iterations is reached.

[0014] In an optional embodiment,

[0015] According to the design parameters, a matching basic unit module is called from a pre-established hierarchical memory IP library, wherein the hierarchical memory IP library contains memory units of different process nodes and performance levels, including:

[0016] Constructing a hierarchical memory IP library, wherein the hierarchical memory IP library includes multiple process node sub-libraries, each process node sub-library includes memory cells of different performance levels, wherein the performance levels include high performance, low power consumption, high density, and general purpose; creating a parameterized design template for each type of memory cell, wherein the template includes capacity configuration parameters, port configuration parameters, performance adjustment parameters, and reliability parameters; extracting key features of each basic unit module and establishing a multi-dimensional index structure;

[0017] Receive and parse design specification documents to extract target process nodes, memory types, capacity requirements, performance requirements, and reliability requirements; analyze application scenarios to prioritize optimization targets; identify and extract key constraints in the design, including area limitations, power budgets, timing requirements, and reliability metrics;

[0018] Based on the process node and memory type, the cells in the hierarchical memory IP library are preliminarily screened; a multi-objective optimization model including area, speed, and power consumption is established, and the extracted constraints are converted into mathematical inequalities; a matching degree calculation function is defined, and the matching degree of each candidate cell module with the requirements is calculated;

[0019] Implement heuristic search based on genetic algorithm, represent candidate units as gene sequences, use matching function as fitness evaluation criterion, optimize candidate solutions through selection, crossover and mutation operations until the termination condition is reached; select the unit module with the highest matching degree and satisfying all constraints;

[0020] Call the parameterized template of the selected unit, set the parameter values ​​according to specific requirements, and generate a customized memory instance; analyze the interface specifications of the target design and generate the necessary interface adaptation layer; use standard test vectors to perform functional verification on the instantiated memory unit, perform timing analysis and power analysis to ensure that the design specifications are met;

[0021] Automatically generate a call report, including the key parameters and performance indicators of the selected unit, and generate a usage guide; establish a feedback mechanism to collect designers' usage experience and improvement suggestions; based on actual application data, regularly update the performance model of the unit module and optimize the matching algorithm;

[0022] Continue to track semiconductor process development trends, develop and verify basic unit modules for new process nodes, and integrate newly developed unit modules into the hierarchical memory IP library; implement version control, record each update content, regularly release updated versions of the library and provide update instructions;

[0023] Among them, the capacity configuration parameters in the parameterized design template include bit width and word count, the port configuration parameters include single-port, dual-port and multi-port options, the performance adjustment parameters include access time, cycle time and power consumption target, and the reliability parameters include ECC support and redundant unit configuration; the multidimensional index structure supports fast search and matching based on characteristics such as area, speed, power consumption and reliability.

[0024] In an optional embodiment,

[0025] Regenerating an optimization plan based on the adjusted strategy, and returning to execute the parallel simulation step until the optimization goal is met or a preset number of iterations is reached, the method further includes:

[0026] Check the final optimized memory IP layout to ensure compliance with process design rules; generate optimized layout files, including GDS II format and OASI S format;

[0027] Based on the optimized layout scheme, the Liberty file, Veril og functional model, SPI CE netlist and test vectors under various working conditions are automatically generated; the performance parameters of the generated IP under different PVT conditions are predicted based on a large amount of historical data using machine learning algorithms;

[0028] Package the optimized memory IP into a standard cell library format and generate design documents containing detailed technical specifications, instructions for use, and performance reports; at the same time, generate abstract models for chip-level simulation and verification;

[0029] Establish IP-level version control and traceability mechanisms to record key decisions and performance evolution during the optimization process; feed back optimization results to the hierarchical memory IP library for continuous improvement and knowledge accumulation.

[0030] In an optional embodiment,

[0031] The neural network model is used to quickly evaluate the initial memory IP layout scheme, and a genetic algorithm is used to generate multiple optimized layout candidate schemes. Each optimized layout candidate scheme includes different basic unit arrangements, metal layer usage strategies, and buffer insertion locations, including:

[0032] Construct a multi-layer perceptron neural network model, wherein the input layer of the neural network model includes layout parameters, and the output layer is a predicted performance indicator; train the neural network model using historical memory IP design data, using a back propagation algorithm and a stochastic gradient descent method to minimize the mean square error between the predicted value and the actual value; and evaluate the generalization ability of the neural network model through cross-validation;

[0033] Converting the initial memory IP layout plan into an input format of a neural network model; performing forward propagation calculations on the initial memory IP layout plan using the trained neural network model to obtain predicted performance indicators; comparing the predicted performance indicators with design goals to evaluate the initial layout plan;

[0034] Designing a genetic algorithm optimization framework, including defining a chromosome encoding scheme, designing a fitness function, and implementing selection operations, crossover operations, and mutation operations, wherein the chromosome encoding scheme maps memory IP layout parameters to a gene sequence, and the fitness function comprehensively considers the weighted sum of speed, power consumption, and area;

[0035] Initialize the population, take the initial layout scheme as an individual in the population, and randomly generate other individuals; use the neural network model to quickly evaluate each individual in the population and calculate the fitness value; perform genetic operations to generate a new generation of population, including selecting individuals with high fitness, performing crossover operations to generate new layout schemes, and performing mutation operations on some individuals; apply an elite retention strategy to ensure that the best individuals are retained;

[0036] Repeat the population evaluation and genetic operation for multiple generations of evolution until the preset termination condition is reached; select multiple individuals with high fitness from the last generation of population as candidate solutions for optimized layout, each candidate solution includes different basic unit arrangements, metal layer usage strategies and buffer insertion positions;

[0037] Conduct detailed circuit simulation on the candidate optimized layout solutions to obtain performance indicators; sort the candidate solutions based on the simulation results and select the optimal layout solution; generate a physical layout for the optimal layout solution, and perform design rule checking and layout and principle verification. Figure 1 consistency check;

[0038] If the optimal layout solution does not meet the design goal, the genetic algorithm parameters or fitness function weights are adjusted, the population initialization step is returned to be executed, and the optimization candidate solution is regenerated; the optimization process is repeated until the design goal is met or the preset number of iterations is reached.

[0039] In an optional embodiment,

[0040] Using deep reinforcement learning algorithms, based on historical optimization data and current verification results, the optimization strategies and weights are dynamically adjusted, including:

[0041] Construct a memory IP layout optimization environment model, the environment model includes a state space, an action space, and a reward function, wherein the state space represents the characteristics of the current layout solution, the action space defines executable optimization operations, and the reward function reflects the optimization effect based on the weighted sum of performance indicators;

[0042] Design a multi-layer neural network as a deep Q network for approximating the Q function, wherein the deep Q network includes an input layer corresponding to the state space, multiple hidden layers using a ReLU activation function, and an output layer corresponding to the action space; implement a fixed-capacity experience replay pool for storing and sampling transfer samples;

[0043] Collect and preprocess historical memory IP layout optimization project data, clean, standardize and extract features from the data; use the processed historical memory IP layout optimization project data to pre-train the deep Q network for supervised learning, build a training set, select mean square error as the loss function, use the Adam optimizer for gradient descent, and use a small batch training method;

[0044] Implement the ε-greedy exploration strategy for action selection, gradually reducing the probability of random exploration during training; introduce the target Q network and set up a regular update mechanism to improve training stability; implement the priority experience replay mechanism, calculate the sample priority based on the temporal difference error, and use the importance sampling weight for deviation correction;

[0045] Execute the main loop of deep reinforcement learning training, including initializing the environment, selecting actions, observing rewards and next states, storing experience, sampling training, and updating network parameters; regularly evaluate the performance of the current optimization strategy, including average reward value, success rate, and convergence speed;

[0046] Dynamically adjust the learning rate according to the performance evaluation results to achieve learning rate adaptation and annealing strategy; track the improvement of each performance indicator, dynamically adjust the weight of each indicator in the reward function, and use soft update to smooth weight changes; dynamically adjust the exploration strategy according to the learning progress, and optimize the exploration-exploitation balance while ensuring continuous environmental exploration.

[0047] In an optional embodiment,

[0048] The Q value corresponding to the target Q network is calculated as follows:

[0049] Q_target=r+γ*max(Q'(s',a'));

[0050] Where Q_target represents the target Q value, r represents the immediate reward, which is used to indicate the immediate feedback given by the environment after executing action a in the current state s; γ represents the discount factor, which ranges from 0 to 1 and is used to balance the importance of immediate rewards and future rewards; Q' represents the target Q network; s' represents the next state, that is, the new state to which the environment transfers after executing action a in the current state s; a' represents the next action, which represents all possible actions that may be taken in state s'; max(Q'(s',a') represents the maximum value of the Q value among all possible actions a' in the next state s'.

[0051] According to a second aspect of the embodiments of the present invention,

[0052] Provides EDA software implementation system for memory IP layout optimization, including:

[0053] The first unit is used to receive memory IP design parameters and optimization targets input by a user, wherein the design parameters include memory type, capacity, bit width, process node, voltage domain, clock domain, power consumption target and area limit; according to the design parameters, call a matching basic unit module from a pre-established hierarchical memory IP library, wherein the hierarchical memory IP library includes memory units of different process nodes and performance levels;

[0054] The second unit is used to generate an initial memory IP layout scheme based on the basic unit module, including determining the memory array structure, peripheral circuit layout and metal layer allocation; using a neural network model to quickly evaluate the initial memory IP layout scheme, and using a genetic algorithm to generate multiple optimized layout candidate schemes, each of which includes a different basic unit arrangement, metal layer usage strategy and buffer insertion position; parallel simulation of the optimized layout candidate schemes to select the optimal layout scheme; post-simulation verification of the optimal layout scheme, including static timing analysis, dynamic power consumption analysis and electromagnetic compatibility analysis;

[0055] The third unit is used to start the adaptive optimization process if the verification result does not meet the design goal: using the deep reinforcement learning algorithm, dynamically adjust the optimization strategy and weight according to the historical optimization data and the current verification result; regenerate the optimization plan based on the adjusted strategy, and return to execute the parallel simulation steps until the optimization goal is met or the preset number of iterations is reached.

[0056] According to a third aspect of the embodiments of the present invention,

[0057] An electronic device is provided, comprising:

[0058] processor;

[0059] a memory for storing processor-executable instructions;

[0060] The processor is configured to call the instructions stored in the memory to execute the aforementioned method.

[0061] According to a fourth aspect of the embodiments of the present invention,

[0062] A computer-readable storage medium is provided, on which computer program instructions are stored. When the computer program instructions are executed by a processor, the aforementioned method is implemented.

[0063] This application uses a neural network model for rapid evaluation and a genetic algorithm to generate optimized layout candidate solutions. This method combines the efficiency of machine learning with the global search capability of evolutionary algorithms, and can generate multiple high-quality memory IP layout solutions in a short period of time, greatly improving design efficiency and optimization effects. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] Figure 1 A flowchart of an EDA software implementation method for memory IP layout optimization according to an embodiment of the present invention;

[0065] Figure 2 A schematic diagram of the structure of an EDA software implementation system for memory IP layout optimization according to an embodiment of the present invention. DETAILED DESCRIPTION

[0066] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0067] The technical solution of the present invention is described in detail with specific embodiments below. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described in detail in some embodiments.

[0068] Figure 1 FIG. 1 is a flow chart of an EDA software implementation method for optimizing memory IP layout according to an embodiment of the present invention, as shown in FIG. Figure 1 As shown, the method includes:

[0069] S101. Receive memory IP design parameters and optimization targets input by a user, wherein the design parameters include memory type, capacity, bit width, process node, voltage domain, clock domain, power consumption target, and area limit; based on the design parameters, call a matching basic unit module from a pre-established hierarchical memory IP library, wherein the hierarchical memory IP library includes memory units of different process nodes and performance levels;

[0070] S102. Generate an initial memory IP layout scheme based on the basic unit module, including determining the memory array structure, peripheral circuit layout and metal layer allocation; use a neural network model to quickly evaluate the initial memory IP layout scheme, and use a genetic algorithm to generate multiple optimized layout candidate schemes, each of which includes a different basic unit arrangement, metal layer usage strategy and buffer insertion position; perform parallel simulation on the optimized layout candidate schemes to select the optimal layout scheme; perform post-simulation verification on the optimal layout scheme, including static timing analysis, dynamic power consumption analysis and electromagnetic compatibility analysis;

[0071] S103. If the verification result does not meet the design goal, the adaptive optimization process is started: using the deep reinforcement learning algorithm, the optimization strategy and weights are dynamically adjusted according to the historical optimization data and the current verification results; the optimization plan is regenerated based on the adjusted strategy, and the parallel simulation steps are returned to execute until the optimization goal is met or the preset number of iterations is reached.

[0072] In an optional embodiment,

[0073] According to the design parameters, a matching basic unit module is called from a pre-established hierarchical memory IP library, wherein the hierarchical memory IP library contains memory units of different process nodes and performance levels, including:

[0074] Constructing a hierarchical memory IP library, wherein the hierarchical memory IP library includes multiple process node sub-libraries, each process node sub-library includes memory cells of different performance levels, wherein the performance levels include high performance, low power consumption, high density, and general purpose; creating a parameterized design template for each type of memory cell, wherein the template includes capacity configuration parameters, port configuration parameters, performance adjustment parameters, and reliability parameters; extracting key features of each basic unit module and establishing a multi-dimensional index structure;

[0075] Receive and parse design specification documents to extract target process nodes, memory types, capacity requirements, performance requirements, and reliability requirements; analyze application scenarios to prioritize optimization targets; identify and extract key constraints in the design, including area limitations, power budgets, timing requirements, and reliability metrics;

[0076] Based on the process node and memory type, the cells in the hierarchical memory IP library are preliminarily screened; a multi-objective optimization model including area, speed, and power consumption is established, and the extracted constraints are converted into mathematical inequalities; a matching degree calculation function is defined, and the matching degree of each candidate cell module with the requirements is calculated;

[0077] Implement heuristic search based on genetic algorithm, represent candidate units as gene sequences, use matching function as fitness evaluation criterion, optimize candidate solutions through selection, crossover and mutation operations until the termination condition is reached; select the unit module with the highest matching degree and satisfying all constraints;

[0078] Call the parameterized template of the selected unit, set the parameter values ​​according to specific requirements, and generate a customized memory instance; analyze the interface specifications of the target design and generate the necessary interface adaptation layer; use standard test vectors to perform functional verification on the instantiated memory unit, perform timing analysis and power analysis to ensure that the design specifications are met;

[0079] Automatically generate a call report, including the key parameters and performance indicators of the selected unit, and generate a usage guide; establish a feedback mechanism to collect designers' usage experience and improvement suggestions; based on actual application data, regularly update the performance model of the unit module and optimize the matching algorithm;

[0080] Continue to track semiconductor process development trends, develop and verify basic unit modules for new process nodes, and integrate newly developed unit modules into the hierarchical memory IP library; implement version control, record each update content, regularly release updated versions of the library and provide update instructions;

[0081] Among them, the capacity configuration parameters in the parameterized design template include bit width and word count, the port configuration parameters include single-port, dual-port and multi-port options, the performance adjustment parameters include access time, cycle time and power consumption target, and the reliability parameters include ECC support and redundant unit configuration; the multidimensional index structure supports fast search and matching based on characteristics such as area, speed, power consumption and reliability.

[0082] In an optional embodiment,

[0083] The neural network model is used to quickly evaluate the initial memory IP layout scheme, and a genetic algorithm is used to generate multiple optimized layout candidate schemes. Each optimized layout candidate scheme includes different basic unit arrangements, metal layer usage strategies, and buffer insertion locations, including:

[0084] Construct a multi-layer perceptron neural network model, wherein the input layer of the neural network model includes layout parameters, and the output layer is a predicted performance indicator; train the neural network model using historical memory IP design data, using a back propagation algorithm and a stochastic gradient descent method to minimize the mean square error between the predicted value and the actual value; and evaluate the generalization ability of the neural network model through cross-validation;

[0085] Converting the initial memory IP layout plan into an input format of a neural network model; performing forward propagation calculations on the initial memory IP layout plan using the trained neural network model to obtain predicted performance indicators; comparing the predicted performance indicators with design goals to evaluate the initial layout plan;

[0086] Designing a genetic algorithm optimization framework, including defining a chromosome encoding scheme, designing a fitness function, and implementing selection operations, crossover operations, and mutation operations, wherein the chromosome encoding scheme maps memory IP layout parameters to a gene sequence, and the fitness function comprehensively considers the weighted sum of speed, power consumption, and area;

[0087] Initialize the population, take the initial layout scheme as an individual in the population, and randomly generate other individuals; use the neural network model to quickly evaluate each individual in the population and calculate the fitness value; perform genetic operations to generate a new generation of population, including selecting individuals with high fitness, performing crossover operations to generate new layout schemes, and performing mutation operations on some individuals; apply an elite retention strategy to ensure that the best individuals are retained;

[0088] Repeat the population evaluation and genetic operation for multiple generations of evolution until the preset termination condition is reached; select multiple individuals with high fitness from the last generation of population as candidate solutions for optimized layout, each candidate solution includes different basic unit arrangements, metal layer usage strategies and buffer insertion positions;

[0089] Conduct detailed circuit simulation on the candidate optimized layout solutions to obtain performance indicators; sort the candidate solutions based on the simulation results and select the optimal layout solution; generate a physical layout for the optimal layout solution, and perform design rule checking and layout and principle verification. Figure 1 consistency check;

[0090] If the optimal layout solution does not meet the design goal, the genetic algorithm parameters or fitness function weights are adjusted, the population initialization step is returned to be executed, and the optimization candidate solution is regenerated; the optimization process is repeated until the design goal is met or the preset number of iterations is reached.

[0091] Exemplarily, based on the memory IP design parameters input by the user, a matching basic unit module is called from a pre-established hierarchical memory IP library, including memory type (such as SRAM, DRAM), capacity, bit width, process node, voltage domain, clock domain, etc.

[0092] Generate an initial memory IP layout plan based on the called basic unit module. This includes determining the memory array structure (such as the number of rows and columns, sub-array division), peripheral circuit layout (such as the location of row decoders, column selectors, and sense amplifiers), and preliminary metal layer allocation strategies.

[0093] A multi-layer perceptron (MLP) neural network model is built to quickly evaluate the performance of the initial layout solution. The input layer of the model includes layout parameters (such as cell arrangement, metal layer usage, buffer location, etc.), and the output layer is the predicted performance indicators (such as access time, power consumption, area, etc.).

[0094] The neural network model is trained using historical memory IP design data. The training data set contains a variety of memory IP layout schemes with different configurations and their corresponding actual performance indicators. The model is trained using the back propagation algorithm and stochastic gradient descent method to minimize the mean square error between the predicted value and the actual value.

[0095] The generalization ability of the model is evaluated by cross-validation techniques, and the prediction accuracy of the model is verified using the test set. If necessary, the network structure (such as the number of layers, the number of neurons) and hyperparameters (such as learning rate, batch size) are adjusted to optimize the model performance.

[0096] Convert the initial memory IP layout scheme into the input format of the neural network model. This includes quantizing the layout parameters into numerical feature vectors, such as the encoding representation of the cell arrangement, the proportion of metal layer usage, the buffer density, etc. Use the trained neural network model to perform forward propagation calculations on the initial layout scheme to quickly obtain predicted performance indicators, including key parameters such as access time, power consumption, and area.

[0097] Compare the prediction results with the design goals to preliminarily evaluate the advantages and disadvantages of the initial layout plan, providing a benchmark and direction for subsequent optimization. Define the chromosome encoding scheme to map the memory IP layout parameters to a gene sequence. For example, integer encoding is used to represent the arrangement order of the basic units, binary encoding is used to represent the use strategy of the metal layer, and real encoding is used to represent the insertion position of the buffer. Design a fitness function that comprehensively considers the weighted sum of multiple performance indicators (such as speed, power consumption, and area). The weights can be dynamically adjusted according to design requirements.

[0098] Implement genetic operation operators, including: selection operation: using roulette selection method or tournament selection method; crossover operation: for different coding parts, implement single-point crossover, two-point crossover or uniform crossover respectively; mutation operation: implement permutation mutation for integer coding part, implement bit flip mutation for binary coding part, and implement Gaussian mutation for real coding part;

[0099] Design an elite retention strategy to ensure that the best individuals in each generation will not be lost during the evolution process. Initialize the population, take the initial layout plan as an individual in the population, and randomly generate other individuals to ensure population diversity. Use a neural network model to quickly evaluate each individual (layout plan) in the population and calculate its fitness value.

[0100] Perform genetic operations to generate a new generation of population: retain individuals with high fitness through selection operations; perform crossover operations on selected individuals to generate new layout plans; perform mutation operations on some individuals to introduce random changes; apply elite retention strategies to ensure that the best individuals are retained to the next generation; repeat multiple generations of evolution until the preset termination conditions (such as maximum number of generations or fitness convergence) are reached.

[0101] Multiple individuals with high fitness are selected from the last generation population as candidates for optimized layout, which contain different basic unit arrangements, metal layer usage strategies, and buffer insertion positions.

[0102] Perform detailed circuit simulation on the generated optimized layout candidates to obtain more accurate performance indicators. This includes static timing analysis, dynamic power analysis, and signal integrity analysis.

[0103] Based on the simulation results, the candidate solutions are ranked and the optimal layout solution is selected.

[0104] Generate physical layout for the selected optimal layout solution, and perform design rule checking (DRC) and layout and principle Figure 1 consistency check (LVS).

[0105] If the optimal solution fails to meet all design objectives, return to step 5, adjust the genetic algorithm parameters (such as crossover rate, mutation rate) or fitness function weights, and regenerate the optimization candidate solution.

[0106] Through the above steps, we have realized the technical solution of using neural network models for rapid evaluation and genetic algorithms to generate optimized layout candidate solutions. This method combines the efficiency of machine learning and the global search capability of evolutionary algorithms, and can generate multiple high-quality memory IP layout solutions in a short time, greatly improving design efficiency and optimization effects.

[0107] In an optional embodiment,

[0108] Using deep reinforcement learning algorithms, based on historical optimization data and current verification results, the optimization strategies and weights are dynamically adjusted, including:

[0109] Construct a memory IP layout optimization environment model, the environment model includes a state space, an action space, and a reward function, wherein the state space represents the characteristics of the current layout solution, the action space defines executable optimization operations, and the reward function reflects the optimization effect based on the weighted sum of performance indicators;

[0110] Design a multi-layer neural network as a deep Q network for approximating the Q function, wherein the deep Q network includes an input layer corresponding to the state space, multiple hidden layers using a ReLU activation function, and an output layer corresponding to the action space; implement a fixed-capacity experience replay pool for storing and sampling transfer samples;

[0111] Collect and preprocess historical memory IP layout optimization project data, clean, standardize and extract features from the data; use the processed historical memory IP layout optimization project data to pre-train the deep Q network for supervised learning, build a training set, select mean square error as the loss function, use the Adam optimizer for gradient descent, and use a small batch training method;

[0112] Implement the ε-greedy exploration strategy for action selection, gradually reducing the probability of random exploration during training; introduce the target Q network and set up a regular update mechanism to improve training stability; implement the priority experience replay mechanism, calculate the sample priority based on the temporal difference error, and use the importance sampling weight for deviation correction;

[0113] Execute the main loop of deep reinforcement learning training, including initializing the environment, selecting actions, observing rewards and next states, storing experience, sampling training, and updating network parameters; regularly evaluate the performance of the current optimization strategy, including average reward value, success rate, and convergence speed;

[0114] Dynamically adjust the learning rate according to the performance evaluation results to achieve learning rate adaptation and annealing strategy; track the improvement of each performance indicator, dynamically adjust the weight of each indicator in the reward function, and use soft update to smooth weight changes; dynamically adjust the exploration strategy according to the learning progress, and optimize the exploration-exploitation balance while ensuring continuous environmental exploration.

[0115] Exemplarily, establishing a memory IP layout optimization environment model includes:

[0116] State space: A representation of the characteristics of the current layout solution, such as cell arrangement, metal layer usage, buffer locations, etc.

[0117] Action space: Optimization operations that can be performed, such as adjusting cell positions, changing metal layer allocations, inserting / deleting buffers, etc.

[0118] Reward function: Based on the weighted sum of performance indicators (such as speed, power consumption, and area), it reflects the optimization effect. A multi-layer neural network is designed as a Q function approximator, and its structure includes:

[0119] Input layer: corresponds to the state space dimension; hidden layer: multiple fully connected layers, using ReLU activation function; output layer: corresponds to the action space dimension, representing the Q value of each action.

[0120] Implement a fixed-capacity experience replay pool to store and sample transfer samples (s, a, r, s') to improve sample utilization efficiency and algorithm stability.

[0121] Collect data from past memory IP layout optimization projects, including initial layout, intermediate states during optimization, final layout, and performance indicators. Clean the data to remove outliers and incomplete samples.

[0122] Standardize the collected data to normalize the features of different scales to the same range. Extract key features such as layout density, critical path delay, power consumption distribution, etc.

[0123] Deep reinforcement learning training process:

[0124] ε-greedy exploration strategy implementation; Implement ε-greedy strategy for action selection:

[0125] Randomly select an action with probability ε (exploration); select the action with the largest Q value with probability 1-ε (exploitation); gradually reduce the ε value as training progresses, linearly decaying from 0.9 to 0.1;

[0126] Target network update mechanism: introduce a target Q network to calculate the target Q value and improve training stability: copy the parameters of the main Q network to the target Q network every C steps (e.g. C = 1000); target Q value calculation: Q_target = r + γ*max(Q'(s',a')), where Q' is the target network;

[0127] Implement a priority experience replay mechanism to improve the learning efficiency of important samples: define sample priority: p_i = |δ_i| + ε, where δ_i is the TD error; calculate sampling probability: P(i) = p_i^α / Σ_k p_k^α; use importance sampling weights for deviation correction;

[0128] Training loop:

[0129] Execute the main deep reinforcement learning training loop:

[0130] a) Initialize the memory IP layout environment; b) Execute the ε-greedy strategy to select action a for the current state s; c) Execute action a, observe the reward r and the next state s'; d) Store the experience (s, a, r, s') into the experience replay pool e) Sample small batches of data from the experience replay pool; f) Calculate the target Q value and loss function; g) Perform gradient descent and update the main Q network parameters; h) Update the target Q network every C steps; i) Repeat steps bh until the termination condition is reached.

[0131] Adaptive learning rate adjustment; dynamically adjust the learning rate based on performance evaluation results: if performance continues to improve, increase the learning rate slightly; if performance fluctuates or decreases, reduce the learning rate; implement a learning rate annealing strategy to gradually reduce the base learning rate as training progresses;

[0132] Reward function weight adaptation: Dynamically adjust the weight of each performance indicator in the reward function: track the improvement of each performance indicator; increase the weight of indicators that improve slowly; appropriately reduce the weight of indicators that have reached the target; use soft update to smooth weight changes: w_new = β*w_old + (1-β)*w_target;

[0133] Dynamic adjustment of exploration strategies:

[0134] Dynamically adjust the ε value based on learning progress: maintain a high exploration rate in the early stages of training; speed up the decay of ε when performance begins to stabilize; set a minimum exploration rate ε_min to ensure continuous environment exploration;

[0135] Verify the current optimal strategy at regular training steps: Run multiple optimizations on the test set and collect performance statistics; compare with baseline methods (such as genetic algorithms); generate detailed performance reports, including the degree of improvement of various indicators;

[0136] Design an interactive interface to allow expert intervention and adjustment: display key decision points in the optimization process; allow experts to manually adjust some optimization decisions; convert expert knowledge into reward signals and integrate them into the reinforcement learning process;

[0137] Establish a continuous learning mechanism to continuously improve model performance: add new optimization cases to the training data set; regularly retrain the model to integrate the latest optimization experience; maintain the model version library and support rollback to the previous stable version;

[0138] Through the above detailed steps, we have implemented an adaptive memory IP layout optimization system based on deep reinforcement learning. The system can effectively use historical optimization data, dynamically adjust optimization strategies and weights according to current verification results, and continuously improve optimization effects and efficiency. This method combines the powerful expression ability of deep learning and the decision-making optimization ability of reinforcement learning, providing an intelligent and adaptive solution to the memory IP layout optimization problem.

[0139] In an optional embodiment,

[0140] The Q value corresponding to the target Q network is calculated as follows:

[0141] Q_target=r+γ*max(Q'(s',a'));

[0142] Where Q_target represents the target Q value, r represents the immediate reward, which is used to indicate the immediate feedback given by the environment after executing action a in the current state s; γ represents the discount factor, which ranges from 0 to 1 and is used to balance the importance of immediate rewards and future rewards; Q' represents the target Q network; s' represents the next state, that is, the new state to which the environment transfers after executing action a in the current state s; a' represents the next action, which represents all possible actions that may be taken in state s'; max(Q'(s',a') represents the maximum value of the Q value among all possible actions a' in the next state s'.

[0143] Figure 2 The schematic diagram of the structure of the EDA software implementation system for memory IP layout optimization according to the embodiment of the present invention is as follows: Figure 2 As shown, the system comprises:

[0144] The first unit is used to receive memory IP design parameters and optimization targets input by a user, wherein the design parameters include memory type, capacity, bit width, process node, voltage domain, clock domain, power consumption target and area limit; according to the design parameters, call a matching basic unit module from a pre-established hierarchical memory IP library, wherein the hierarchical memory IP library includes memory units of different process nodes and performance levels;

[0145] The second unit is used to generate an initial memory IP layout scheme based on the basic unit module, including determining the memory array structure, peripheral circuit layout and metal layer allocation; using a neural network model to quickly evaluate the initial memory IP layout scheme, and using a genetic algorithm to generate multiple optimized layout candidate schemes, each of which includes a different basic unit arrangement, metal layer usage strategy and buffer insertion position; parallel simulation of the optimized layout candidate schemes to select the optimal layout scheme; post-simulation verification of the optimal layout scheme, including static timing analysis, dynamic power consumption analysis and electromagnetic compatibility analysis;

[0146] The third unit is used to start the adaptive optimization process if the verification result does not meet the design goal: using the deep reinforcement learning algorithm, dynamically adjust the optimization strategy and weight according to the historical optimization data and the current verification result; regenerate the optimization plan based on the adjusted strategy, and return to execute the parallel simulation steps until the optimization goal is met or the preset number of iterations is reached.

[0147] The present invention may be a method, an apparatus, a system and / or a computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for executing various aspects of the present invention.

[0148] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. An EDA software implementation method for memory IP layout optimization, characterized in that: include: receiving memory IP design parameters and optimization targets input by a user, wherein the design parameters include memory type, capacity, bit width, process node, voltage domain, clock domain, power consumption target, and area limit; calling a matching basic unit module from a pre-established hierarchical memory IP library according to the design parameters, wherein the hierarchical memory IP library includes memory units of different process nodes and performance levels; Generate an initial memory IP layout scheme based on the basic unit module, including determining the memory array structure, peripheral circuit layout and metal layer allocation; use a neural network model to quickly evaluate the initial memory IP layout scheme, and use a genetic algorithm to generate multiple optimized layout candidate schemes, each of which includes a different basic unit arrangement, metal layer usage strategy and buffer insertion position; perform parallel simulation on the optimized layout candidate schemes to select the optimal layout scheme; perform post-simulation verification on the optimal layout scheme, including static timing analysis, dynamic power consumption analysis and electromagnetic compatibility analysis; If the verification result does not meet the design goal, the adaptive optimization process is started: using the deep reinforcement learning algorithm, the optimization strategy and weight are dynamically adjusted according to the historical optimization data and the current verification result; The optimization plan is regenerated based on the adjusted strategy, and the parallel simulation steps are executed again until the optimization goal is met or the preset number of iterations is reached.

2. The method according to claim 1, characterized in that According to the design parameters, a matching basic unit module is called from a pre-established hierarchical memory IP library, wherein the hierarchical memory IP library contains memory units of different process nodes and performance levels, including: Constructing a hierarchical memory IP library, wherein the hierarchical memory IP library includes multiple process node sub-libraries, each process node sub-library includes memory cells of different performance levels, wherein the performance levels include high performance, low power consumption, high density, and general purpose; creating a parameterized design template for each type of memory cell, wherein the template includes capacity configuration parameters, port configuration parameters, performance adjustment parameters, and reliability parameters; extracting key features of each basic unit module and establishing a multi-dimensional index structure; Receive and parse design specification documents to extract target process node, memory type, capacity requirements, performance requirements, and reliability requirements; Analyze application scenarios and determine the priority of optimization goals; Identify and extract key constraints in the design, including area limitations, power budgets, timing requirements, and reliability metrics; Based on the process node and memory type, the cells in the hierarchical memory IP library are preliminarily screened; a multi-objective optimization model including area, speed, and power consumption is established, and the extracted constraints are converted into mathematical inequalities; Define a matching degree calculation function to calculate the matching degree between each candidate unit module and the requirements; Implement heuristic search based on genetic algorithm, represent candidate units as gene sequences, use matching function as fitness evaluation criterion, optimize candidate solutions through selection, crossover and mutation operations until the termination condition is reached; select the unit module with the highest matching degree and satisfying all constraints; Call the parameterized template of the selected unit, set the parameter values ​​according to specific requirements, and generate a customized memory instance; analyze the interface specifications of the target design and generate the necessary interface adaptation layer; Use standard test vectors to verify the functionality of the instantiated memory cells, perform timing analysis and power analysis to ensure that the design specifications are met; Automatically generate call reports, including key parameters and performance indicators of the selected unit, and generate usage guidelines; Establish a feedback mechanism to collect designers’ usage experience and improvement suggestions; Based on actual application data, the performance model of the unit module is regularly updated to optimize the matching algorithm; Continue to track semiconductor process development trends, develop and verify basic unit modules for new process nodes, and integrate newly developed unit modules into the hierarchical memory IP library; implement version control, record each update content, regularly release updated versions of the library and provide update instructions; Among them, the capacity configuration parameters in the parameterized design template include bit width and word count, the port configuration parameters include single-port, dual-port and multi-port options, the performance adjustment parameters include access time, cycle time and power consumption target, and the reliability parameters include ECC support and redundant unit configuration; the multidimensional index structure supports fast search and matching based on characteristics such as area, speed, power consumption and reliability.

3. The method according to claim 1, characterized in that Regenerating an optimization plan based on the adjusted strategy, and returning to execute the parallel simulation step until the optimization goal is met or a preset number of iterations is reached, the method further includes: Check the final optimized memory IP layout to ensure compliance with process design rules; generate optimized layout files, including GDS II format and OASIS format; Based on the optimized layout scheme, Liberty files, Verilog functional models, SPICE netlists and test vectors are automatically generated under various working conditions. The performance parameters of the generated IP under different PVT conditions are predicted based on a large amount of historical data using machine learning algorithms. Package the optimized memory IP into a standard cell library format and generate design documents containing detailed technical specifications, instructions for use, and performance reports; at the same time, generate abstract models for chip-level simulation and verification; Establish IP-level version control and traceability mechanisms to record key decisions and performance evolution during the optimization process; feed back optimization results to the hierarchical memory IP library for continuous improvement and knowledge accumulation.

4. The method according to claim 1, characterized in that The neural network model is used to quickly evaluate the initial memory IP layout scheme, and a genetic algorithm is used to generate multiple optimized layout candidate schemes. Each optimized layout candidate scheme includes different basic unit arrangements, metal layer usage strategies, and buffer insertion locations, including: Construct a multi-layer perceptron neural network model, wherein the input layer of the neural network model includes layout parameters, and the output layer is a predicted performance indicator; train the neural network model using historical memory IP design data, using a back propagation algorithm and a stochastic gradient descent method to minimize the mean square error between the predicted value and the actual value; and evaluate the generalization ability of the neural network model through cross-validation; Converting the initial memory IP layout plan into an input format of a neural network model; performing forward propagation calculations on the initial memory IP layout plan using the trained neural network model to obtain predicted performance indicators; comparing the predicted performance indicators with design goals to evaluate the initial layout plan; Designing a genetic algorithm optimization framework, including defining a chromosome encoding scheme, designing a fitness function, and implementing selection operations, crossover operations, and mutation operations, wherein the chromosome encoding scheme maps memory IP layout parameters to a gene sequence, and the fitness function comprehensively considers the weighted sum of speed, power consumption, and area; Initialize the population, take the initial layout scheme as an individual in the population, and randomly generate other individuals; use the neural network model to quickly evaluate each individual in the population and calculate the fitness value; perform genetic operations to generate a new generation of population, including selecting individuals with high fitness, performing crossover operations to generate new layout schemes, and performing mutation operations on some individuals; apply an elite retention strategy to ensure that the best individuals are retained; Repeat the population evaluation and genetic operation for multiple generations of evolution until the preset termination condition is reached; select multiple individuals with high fitness from the last generation of population as candidate solutions for optimized layout, each candidate solution includes different basic unit arrangements, metal layer usage strategies and buffer insertion positions; Perform detailed circuit simulation on the candidate optimized layout solutions to obtain performance indicators; sort the candidate solutions based on the simulation results and select the optimal layout solution; generate a physical layout for the optimal layout solution, and perform design rule checks and layout and schematic consistency checks; If the optimal layout solution does not meet the design goal, the genetic algorithm parameters or fitness function weights are adjusted, the population initialization step is returned to be executed, and the optimization candidate solution is regenerated; the optimization process is repeated until the design goal is met or the preset number of iterations is reached.

5. The method according to claim 1, characterized in that Using deep reinforcement learning algorithms, based on historical optimization data and current verification results, the optimization strategies and weights are dynamically adjusted, including: Construct a memory IP layout optimization environment model, the environment model includes a state space, an action space, and a reward function, wherein the state space represents the characteristics of the current layout solution, the action space defines executable optimization operations, and the reward function reflects the optimization effect based on the weighted sum of performance indicators; Design a multi-layer neural network as a deep Q network for approximating the Q function, wherein the deep Q network includes an input layer corresponding to the state space, multiple hidden layers using a ReLU activation function, and an output layer corresponding to the action space; implement a fixed-capacity experience replay pool for storing and sampling transfer samples; Collect and preprocess historical memory IP layout optimization project data, clean, standardize and extract features from the data; use the processed historical memory IP layout optimization project data to pre-train the deep Q network for supervised learning, build a training set, select mean square error as the loss function, use the Adam optimizer for gradient descent, and use a small batch training method; Implement the ε-greedy exploration strategy for action selection, gradually reducing the probability of random exploration during training; introduce the target Q network and set up a regular update mechanism to improve training stability; implement the priority experience replay mechanism, calculate the sample priority based on the temporal difference error, and use the importance sampling weight for deviation correction; Execute the main loop of deep reinforcement learning training, including initializing the environment, selecting actions, observing rewards and next states, storing experience, sampling training, and updating network parameters; regularly evaluate the performance of the current optimization strategy, including average reward value, success rate, and convergence speed; Dynamically adjust the learning rate according to the performance evaluation results to achieve learning rate adaptation and annealing strategy; track the improvement of each performance indicator, dynamically adjust the weight of each indicator in the reward function, and use soft update to smooth weight changes; dynamically adjust the exploration strategy according to the learning progress, and optimize the exploration-exploitation balance while ensuring continuous environmental exploration.

6. The method according to claim 5, characterized in that The Q value corresponding to the target Q network is calculated as follows: Q_target=r+γ*max(Q'(s',a')); Where Q_target represents the target Q value, r represents the immediate reward, which is used to indicate the immediate feedback given by the environment after executing action a in the current state s; γ represents the discount factor, which ranges from 0 to 1 and is used to balance the importance of immediate rewards and future rewards; Q' represents the target Q network; s' represents the next state, that is, the new state to which the environment transfers after executing action a in the current state s; a' represents the next action, which represents all possible actions that may be taken in state s'; max(Q'(s',a') represents the maximum value of the Q value among all possible actions a' in the next state s'.

7. An EDA software implementation system for memory IP layout optimization, used to implement the method according to any one of claims 1 to 6, characterized in that: include: The first unit is used to receive memory IP design parameters and optimization targets input by a user, wherein the design parameters include memory type, capacity, bit width, process node, voltage domain, clock domain, power consumption target and area limit; according to the design parameters, call a matching basic unit module from a pre-established hierarchical memory IP library, wherein the hierarchical memory IP library includes memory units of different process nodes and performance levels; The second unit is used to generate an initial memory IP layout scheme based on the basic unit module, including determining the memory array structure, peripheral circuit layout and metal layer allocation; using a neural network model to quickly evaluate the initial memory IP layout scheme, and using a genetic algorithm to generate multiple optimized layout candidate schemes, each of which includes a different basic unit arrangement, metal layer usage strategy and buffer insertion position; parallel simulation of the optimized layout candidate schemes to select the optimal layout scheme; post-simulation verification of the optimal layout scheme, including static timing analysis, dynamic power consumption analysis and electromagnetic compatibility analysis; The third unit is used to start the adaptive optimization process if the verification result does not meet the design goal: using the deep reinforcement learning algorithm to dynamically adjust the optimization strategy and weight according to the historical optimization data and the current verification result; The optimization plan is regenerated based on the adjusted strategy, and the parallel simulation steps are executed again until the optimization goal is met or the preset number of iterations is reached.

8. An electronic device, characterized in that: include: processor; a memory for storing processor-executable instructions; The processor is configured to call the instructions stored in the memory to execute the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having computer program instructions stored thereon, characterized in that: When the computer program instructions are executed by a processor, the method according to any one of claims 1 to 6 is implemented.

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