CN112001140B - Useful deviation time sequence optimization method based on PSO algorithm - Google Patents
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Abstract
The invention discloses a useful deviation time sequence optimization method based on a PSO algorithm, which comprises the following steps: s1, selecting path common points and adding the common points into a buffer; s2, taking the path common point as a particle, taking the buffer quantity as a particle position, taking the sum of worst violations of the establishing time and the holding time as a global optimal solution, and obtaining the optimal buffer quantity by adopting a PSO algorithm, wherein the steps of: s21, randomly generating initial particle positions; s22, inputting the initial particle position into a skew function to obtain an individual optimal solution and a global optimal solution; s23, entering a circulation, inputting the particle position into a skew function, updating the individual optimal solution, and obtaining the current round of global optimal solution; s24, if the global optimal solution of the current round is larger than the global optimal solution, the global optimal solution of the current round is used as the global optimal solution, otherwise, the global optimal solution is not changed; s25, updating the speed; and S26, updating the particle position, if continuing to loop to step S23, and if ending the loop, outputting the particle position.
Description
Technical Field
The invention relates to the technical field of chip physical design static time sequences, in particular to a useful deviation time sequence optimization method based on a PSO algorithm.
Background
As the design requirements of integrated circuits are higher, the circuit scale increases, the combinational logic level increases, and it is more difficult to make the circuit reach timing closure. The timing closure of an integrated circuit consists of two parts, namely, keeping time closure and establishing time closure. For circuits, hold time (hold) is directly related to the circuit function, which can cause problems if the hold time does not converge. The setup time (setup) is related to the frequency of the circuit, and if the setup time is not converged, the operating frequency of the circuit may not meet the design requirement. Therefore, in engineering projects, in order to ensure that the functions of the chip operate normally, the hold time violation should be eliminated first, and then the setup time violation should be repaired as much as possible.
The clock of an integrated circuit is usually tree-shaped, i.e. the work difficulty of timing repair is simplified by ensuring that the clock trees reaching each node are the same length. But there is a conflict between the setup time and the hold time for reaching the same point. That is, if there is a certain margin (slack) in the hold time at a certain point and there is a violation in the setup time, the margin of the hold time can be "borrowed" into the setup time by changing the length of the clock tree at this point, reducing the margin of the hold time and also reducing the violation in the setup time. This method is called useful bias (usefull skew) optimization. But this situation may also result in a new hold time violation being generated when optimizing the setup time. Therefore, two factors are combined to optimize the timing sequence.
Disclosure of Invention
In order to solve the defects of the prior art and achieve the purpose of reducing the violation of the retention time when the useful deviation is optimized to establish the time, the invention adopts the following technical scheme:
a useful deviation time sequence optimization method based on a PSO algorithm comprises the following steps:
s1, selecting path common points and adding the common points into a buffer;
s2, taking the path common points as particles, taking the buffer number as the Particle positions, taking the sum of worst violations of the setup time and the hold time as a global optimal solution, and obtaining the optimal buffer number by adopting a PSO (Particle Swarm Optimization) algorithm, wherein the method comprises the following steps:
s21, randomly generating initial particle positions;
s22, inputting the initial particle position into a skew function to obtain an individual optimal solution and a global optimal solution;
s23, entering a circulation, inputting the particle position into a skew function, updating the individual optimal solution, and obtaining the current round of global optimal solution;
s24, if the global optimal solution of the current round is larger than the global optimal solution, the global optimal solution of the current round is used as the global optimal solution, otherwise, the global optimal solution is unchanged;
s25, updating the speed according to the particle position, the global optimal solution and the inertia weight:
wherein vel is velocity, w is inertial weight, r1, r2 are random numbers between 0 and 1, c1, c2 are learning factors, pos is particle position, pbest is individual optimal solution, gbest is global optimal solution;
and S26, updating the particle position according to the updated speed:
and judging whether the circulation meets the end condition, if not, entering the step S23, and if so, outputting the particle position, namely obtaining the optimized number of the buffers.
The PSO algorithm is applied to the signature and check stage of time sequence analysis of chip physical design, and is mainly used for optimizing and establishing time violation and improving the running frequency of the chip. In a chip system, a plurality of groups of ports are provided, each group of ports is provided with a path common point, buffers are added to the common points to adjust the length of a clock tree, the number of the buffers added to each path common point is taken as an independent variable of a function, the time sequence of the group is repaired by adjusting the independent variable, after the time sequences of the groups with the worst time sequence on the chip are repaired, the whole time sequence of the chip is also repaired, the simulation result of the optimization mode is more real, and the follow-up establishment time and holding time violation analysis can be carried out.
The inertial weight decreases with increasing number of iterations:
whereintFor the current number of iterations, T is the set maximum number of iterations, and wmax and wmin are ranges of w. The larger the inertia weight is, the smaller the influence of the global optimal solution and the individual optimal solution on the speed and the particle position is, and the possibility of falling into the local optimal solution is high.
The skew function converts the number of the buffers into commands in the PT tool through the script, carries out time sequence analysis through the PT tool, reports new optimized holding time and worst time violation, and sums the holding time and the worst time violation through the script and outputs a global optimal solution.
And acquiring a port corresponding to the worst setup time violation, and reporting the worst hold time violation of the port. The method is used for eliminating the influence of other holding time violations on optimization, and finally, a situation that the setup time is greatly optimized and the holding time violations are not much increased is achieved, namely the situation that the setup time violations are repaired in an ideal way.
In step S1, a point on a group of port paths to be repaired is taken as a common point of the paths, and performing the useful bias optimization at this point can effectively reduce the number of particles in the optimization algorithm.
A drawing step is added between the steps S24 and S25, the relation between the current cycle number and the global optimal solution is drawn, the optimization effect can be seen more visually, and the optimization iteration number is set conveniently.
The skew function sets the processes of inputting the number of the buffers to the script, acquiring new optimized holding time from the script and establishing the worst time violation as a cycle, and actually increases the times of time sequence analysis by times, so that the result of each PSO cycle has a higher reference value.
The invention has the advantages and beneficial effects that:
on the basis of the analysis of the violation of the setup time and the hold time in the original static time sequence analysis of the physical design of the chip, the setup time is optimized by using a useful deviation optimization method, and the PSO algorithm is combined, so that the violation of the hold time on the basis of optimizing the setup time is better ensured to be smaller, the function of the chip is ensured, and the running frequency of the chip is improved.
Drawings
FIG. 1 is a schematic diagram of the PSO algorithm of the present invention.
Fig. 2 is a flow chart of the overall operation of the method of the present invention.
FIG. 3 is a flowchart illustrating the operation of the skew script in the present invention.
FIG. 4 is a flowchart illustrating the operation of get _ wns script in the present invention.
Detailed Description
The following detailed description of embodiments of the invention refers to the accompanying drawings. It should be understood that the detailed description and specific examples, while indicating the present invention, are given by way of illustration and explanation only, not limitation.
The PSO algorithm is a model for simulating a bird swarm to find food, that is, when a certain bird finds food, the overall flight direction of the bird swarm is propelled in the direction, and finally the global optimum (gbest) and the individual optimum (pbest) of each particle are achieved. This algorithm is typically used to find the most valued of the function.
As shown in fig. 1, initialization of some variables including the number of particles, the maximum velocity, the range of positions (positions) of the particles, and a function, which is skew, is performed first. An initial pos is generated by using a random number, and a skew function is called to obtain pbest and gbest at the position.
Then the algorithm enters a loop, the loop frequency is adjustable, and the algorithm is set to be 100 times in the invention in consideration of time cost. In the loop, pbest and the maximum value itr _ best obtained in the current loop are updated according to pos, namely gbest obtained in the current loop. Itr _ best is compared with the original gbest, if itr _ best is larger than the original gbest, the gbest is updated, otherwise the gbest is not changed. And drawing is carried out next step, the abscissa is the current cycle number, and the ordinate is the gbest, so that the optimization effect can be seen more visually, and the optimization iteration number can be set conveniently. Then, the kernel of the PSO algorithm is obtained, that is, according to the relationship between the current position and the gbest and the weight updating speed:
whereinwIs the weight of the inertia, and,r 1、r 2is a random number between (0, 1).c 1、c 2Is a learning factor. Then according tovelUpdatingpos：
As can be seen from the formula, the larger the inertial weight is, the less the velocity and the position are influenced by the global optimal solution and the individual optimal solution, and the local optimal solution may be involved. In order to overcome the problem, the inertia weight is set to be reduced along with the increase of the iteration times, so that the influence of the later optimal solution on the speed and the position is increased, and the bootstrap program finds the global optimal solution. Is shown in the following formula, whereintAnd T is the set maximum iteration number for the current iteration number. In the invention is provided withw max=0.9，w min=0.4, decreasing as the number of cycles increases.
In the timing optimization, the global optimum is set as a case where the sum of worst violations (WNS) of the setup time and the hold time is as close to 0 as possible, i.e., a case where the timing is the best (WNS itself is a negative number, and the maximum is 0). The particles are common points of the paths where the timing violation exists at present. Namely, the time sequence result is regarded as a function, and the useful deviation in the useful deviation optimization method is regarded as an independent variable of the function. By adjusting the useful deviation, a situation is finally reached where the setup time is optimized substantially without much larger hold time violations, i.e. the situation of the setup time violations in the ideal is repaired.
As shown in fig. 2, in the useful bias timing optimization method based on the PSO algorithm, first, the hold time violation is almost 0 in the item, and under the condition that the setup time violation is not optimized, a group of ports where the setup time violation is most difficult to repair is found out. The port which catches the set of the establishment time violation is at a point on a path in the establishment time timing report, and the point is usually a common point of a plurality of paths which establish the time violation path, and the number of particles in the optimization algorithm can be effectively reduced by carrying out the useful bias optimization at the point.
The entry point for a useful bias optimization method is to add a buffer (buffer) to adjust the length of the clock tree, i.e., the argument that takes the number of buffers added per path common point as a function, namely pos. Compared with other methods, the result simulated by the optimization method is more real, and subsequent establishment time and holding time violation analysis can be performed. Therefore, the common point of each path is set as a particle, and the number of buffers added to the common point of the path is set as an entry point for optimizing the particle timing. The invention utilizes Prime Time software of Synopsys company to carry out experiments, a PT tool is usually used for signature timing analysis, and the tool can simulate the influence of the PT tool on the establishing Time and the holding Time according to various parameters of the buffer after the buffer is inserted, and further calculate the change of the establishing Time and the holding Time.
And at the initial stage of operation, randomly generating the number of buffers added to the common point of each path, wherein the generated number of buffers is the input of the skew function. This number is translated into commands in the PT tool using scripts. When the PT tool performs timing analysis, the command just generated is read, and then timing analysis after adding a buffer is performed. And reporting a new worst default of the holding time caused by optimization in the time sequence analysis, and then reporting a worst default of the establishing time of the group for optimization. Adding the two results, inputting the result into a PSO algorithm as a function value of a skew function under the condition that the number of buffers at the moment is input, carrying out optimization direction analysis according to the result and the above formula by the algorithm, generating the number of new buffers added with the common points of the paths, further carrying out the next cycle, wherein the cycle number can be set at will, and the optimal result, namely the optimal number of buffers, is obtained after several cycles.
In the present invention, in order to optimize the setup time and the hold time simultaneously, the results of the two terms are added. To eliminate the impact of other hold time violations on the optimization, only the points associated with the endpoints in the setup time report are screened for reporting when a hold time violation is reported. As shown in fig. 3, when the holding times of the endpoints are not violated, the holding times are uniformly set to 0, so that the influence on the optimization of the setup time is avoided.
As shown in fig. 4, in the operation process of the skew function, a loop is also included, in order to make the result of each PSO loop have a reference value, the number of cycles of the skew in the experiment is recorded as N and is set to 8, so that the timing analysis is actually performed 800 times in 100 rounds of PSO algorithm loops.
In a system-on-chip, there are multiple groups of ports, each group of ports has a path common point, and adding a buffer to the common point can repair the timing of the group. After the time sequences of the groups with the worst time sequence on the chip are repaired, the time sequence of the whole chip is also repaired.
The above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.
Claims (4)
1. A useful deviation time sequence optimization method based on a PSO algorithm is characterized in that the PSO algorithm is applied to a signature stage of time sequence analysis of chip physical design, and the method comprises the following steps:
s1, selecting a common point of the path, taking the last point of a group of port paths with the most difficult time violation to be repaired as the common point of the path, and adding the common point into a buffer;
s2, using the common points of the paths as particles, using the number of buffers as particle positions, converting the number of the buffers into commands in a PT tool through a script by a skew function, carrying out time sequence analysis through the PT tool, obtaining ports corresponding to worst setup time violations, reporting the worst hold time violations to the ports, uniformly setting the worst hold time violations to 0 when the hold times of the ports are not violated, using the sum of the worst setup time violations and the worst hold time violations as a global optimal solution, and obtaining the optimal number of the buffers by adopting a PSO algorithm, wherein the method comprises the following steps:
s21, randomly generating initial particle positions;
s22, inputting the initial particle position into a skew function to obtain an individual optimal solution and a global optimal solution;
s23, entering a circulation, inputting the particle position into a skew function, updating the individual optimal solution, and obtaining the current round of global optimal solution;
s24, if the global optimal solution of the current round is larger than the global optimal solution, the global optimal solution of the current round is used as the global optimal solution, otherwise, the global optimal solution is unchanged;
s25, updating the speed according to the particle position, the global optimal solution and the inertia weight:
wherein vel is velocity, w is inertial weight, r1, r2 are random numbers between 0 and 1, c1, c2 are learning factors, pos is particle position, pbest is individual optimal solution, gbest is global optimal solution;
and S26, updating the particle position according to the updated speed:
and judging whether the circulation meets the end condition, if not, entering the step S23, and if so, outputting the particle position, namely obtaining the optimized number of the buffers.
2. The PSO algorithm-based useful bias timing optimization method of claim 1, wherein the inertial weight decreases with increasing number of iterations:
whereintFor the current number of iterations, T is the set maximum number of iterations, and wmax and wmin are ranges of w.
3. The PSO algorithm-based method for optimizing timing of useful bias according to claim 1, wherein a mapping step is added between steps S24 and S25 to map the relationship between the current cycle number and the global optimal solution.
4. The PSO algorithm-based method for optimizing timing of useful skew as claimed in claim 1, wherein the skew function loops the number of input buffers to the script and the process of obtaining the optimized new hold time and establishing the worst-case time violation from the script.
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Inventor after: Huang Kai Inventor after: Li Peng Inventor after: Li Licheng Inventor after: Xi Wei Inventor after: Zeng Xiangjun Inventor after: Yin Xianggen Inventor after: Song Yitong Inventor after: Zheng Dandan Inventor before: Huang Kai Inventor before: Li Peng Inventor before: Li Licheng Inventor before: Xi Wei Inventor before: Zeng Xiangjun Inventor before: Yin Xianggen Inventor before: Song Yitong Inventor before: Zheng Dandan |