CN108763726A - One kind is based on improvement population VLSI fixed border layout planning methods - Google Patents
One kind is based on improvement population VLSI fixed border layout planning methods Download PDFInfo
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/30—Circuit design
- G06F30/39—Circuit design at the physical level
- G06F30/392—Floor-planning or layout, e.g. partitioning or placement
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- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/30—Circuit design
- G06F30/36—Circuit design at the analogue level
- G06F30/367—Design verification, e.g. using simulation, simulation program with integrated circuit emphasis [SPICE], direct methods or relaxation methods
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- G06F—ELECTRIC DIGITAL DATA PROCESSING
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Abstract
The present invention relates to a kind of based on the VLSI fixed border layout planning methods for improving population, convert fixed border floor planning problem to population optimization, and the optimal value of the white area requirements of lowest empty must be reached with improved discussion mechanism Chaos particle swarm optimization algorithm, optimal floor planning is carried out to entire module according to obtained optimal value.The present invention not only has the characteristics that success rate is high, optimizing is fast, but also this layout method based on improved particle swarm optimization algorithm can quickly obtain effective solution of fixed border floor planning.
Description
Technical field
The present invention relates to super large-scale integration fixed border topologies fields, specifically based on improvement population
VLSI fixed border layout planning methods.
Background technology
IC industry is continued to develop along Moore's Law, and chip integration sharply increases, and causes chip design complicated
Property greatly increase, multi-layer design be to solve the problems, such as this important means.In multi-layer layout-design flow, each level cloth
G- Design determines the shape and area of the module of this level processing, as the object of next hierarchic design, carries out thinner Butut
Planning, therefore be all the floor planning design for handling fixed border module from the layout-design after the second level.Traditional planning
The profile that the free frame layout method that method uses obtains is uncertain, therefore differs and surely meet the determining mould of last layer design
Block frame cannot be applicable in modern multi-layer design method.
Domestic and international researcher has carried out particular study to fixed border floor planning problem, and Adya and Markov are first
It first proposes the fixed border Layout Algorithm based on simulated annealing, defines new object function and relevant operation.Liu et al. people
The FOFP methods of profile amplification are proposed, and are used for fixed border Butut.Pengli Ji et al., which are directed to, has soft mode block
The fixed border floor planning of problem, it is proposed that a kind of to merge the layering partitioning algorithm (IMP) being packaged based on iteration.With claimed
It is that the popular packing algorithm of zero dead space (ZDS) is compared, IMP can handle more problems, and the aspect ratio boundary on rectangle is tighter
It is close, the gap bigger between rectangle.Behnam Khodabandeloo et al. propose one and attempt to reduce chip peak temperature
Two benches fixed border floor planning frame.It is compared with HotFloorplan and UFO in GSRC and MCNC benchmark tests,
The frame proposed greatly reduces the peak temperature of chip, and reduces run time.De-xuan ZOU et al. propose a kind of
The modern plane constrained with fixed border is handled based on the method for enhanced simulated annealing (MSA) and superzone domain model
Layout drawing.And tested on six groups of reference circuits, compared with several existing methods, the method proposed is in chip area, line length
It is more effective that relevant target function value aspect is constrained with fixed border.Qi Xu et al. propose a kind of quick heat analysis
Method, and it is used for 3D fixed border floor plannings.Compared with the superposition of thermal profile method, the heat analysis method proposed can incite somebody to action
Average peak temperature reduces by 6.7%, and run time is shorter, and demonstrates the validity and validity of heat analysis method.
Jai-Ming Lin et al. propose a kind of fixed border layout planning method of TSV perception, it can be constrained in fixing profile
Under the conditions of consider line length and routability simultaneously.The experimental results showed that the method proposed can be in significant reduction 3D integrated circuits
Routing congestion and slightly increase line length.Generation people Du et al. promote chip cloth to reduce the line length and blank area of chip
A kind of speed of figure, it is proposed that improvement multivoltage layout method considering fixed border constraint.
To sum up, the shortcomings of traditional layout method has optimizing relatively low, and success rate is not high, and fixed border floor planning
Characteristic with combinatorial optimization problem, better chip layout scheme, the main thought of research are to consider chip face in order to obtain
Product and line length, the shape of binding modules, other constraintss such as fuel factor of chip get off to reach best Butut scheme.
Invention content
In view of this, the purpose of the present invention is to provide a kind of based on the VLSI fixed border Bututs rule for improving population
The method of drawing, effective solution for quickly obtaining fixed border floor planning.
To achieve the above object, the present invention adopts the following technical scheme that：
A kind of VLSI fixed border layout planning methods based on improvement population, it is characterised in that：
Step S1:Convert fixed border floor planning problem to population optimization；
Step S2:The module of chip is expressed as to the character of corresponding particle, and using the representation of character string sequence
Topological relation between module；
Step S3:The optimal value of the white area requirements of lowest empty must be reached with particle cluster algorithm is improved；
Step S4：Optimal floor planning is carried out to entire module according to obtained optimal value.
Further, the step S1 is specially：
The object function of fixed border floor planning is defined as
F (F)=A (1)
Wherein：A indicates chip area.
Further, the step S2 is specially:If some particle is Pi, then character string sequence represent corresponding Butut
Each character λ of scheme, particle represents a module, can obtain particle PiBe encoded to:
Pi=< λ1,λ2, λ3..., λn> (1≤λ≤n, λi≠λj, wherein i ≠ j) and (2)
Further, the laying method of the module is:
A. the module representated by particle first character will be placed on the lower left corner of fixed border；
B. other all modules are put successively according to sequence from left to right；
If c. the remaining space of fixed border not enough places next module, newline puts the module.
Further, the discussion mechanism Chaos particle swarm optimization algorithm is specially：
S31:Whole Butut schemes are initialized, logistic transformation are carried out to first half, μ=4 make all cloth
Figure scheme is in Complete Chaos state, and latter half is generated at random；
S32:Calculate the area of each Butut scheme；
S33:The Butut scheme of single area minimum value is preserved, the Butut scheme of entire area minimum is updated；
S34:According to the Butut scheme that following formula update is whole：
Vi k+1=ω Vi k+c1r1(PLbest-Pi k)+c2r2(PGbest-Pi k) (3)
Pi k+1=Pi k+Vi k+1 (4)
Wherein V indicates that the speed of particle, P indicate that the position of particle, ω are inertia weight, and c1 and c2 are the study more than O
The factor, r1 and r2 are that equally distributed random number, P are obeyed in [0,1] rangeGbestIt is global optimum, PLbestIt is local optimum
Value；
S35:Expand population and mutation process, each step all update PGbestAnd PLbest；
S36:Cycle carries out step S32-235 and obtains the optimal value for meeting preset condition and export result.
Further, the step S35 is specially：
A. P is judged to each particleLbestIt repeats whether constant number reaches the upper limit, reaches, carry out mutation operation, compare
As a result, it is more excellent, replace reservation；
B. judge the optimal P of the group of two populationsGbest1 and PGbestWhether 2 numbers of repetition reach the upper limit, reach, and use
The process of initialization expands population scale；
C. to two populations, each population repeats certain number, randomly selects a particle every time into row variation and compare
As a result, it is more excellent, retain；
D. certain number is repeated, a particle is respectively randomly selected from two populations every time and comes out, according to Inew=uI1+
(1-u)I2, in formula：InewThe offspring individual generated for 2 individual fusions； I1And I2Expression receives 2 individuals of mixing operation；u
For the random number between one 0 to 1, along with random perturbation generates new particle, comparison result is more excellent, replaces reservation；
E. to the P of two populationsGbest1 and PGbest2 carry out the fusion process of d steps；
F. certain number is repeated, mutation process is carried out to particle optimal in two populations；
G. to PGbestSubtle adjustment is carried out, uncomfortable whole sequence of modules, only random adjustment module is vertical and horizontal.
The present invention has the advantages that compared with prior art：
The present invention proposes a kind of VLSI fixed border layout planning methods based on improvement population, excellent using population
Change algorithm and use character string sequence representation method, devises New Algorithm operating method.It is calculated in conjunction with discussion mechanism Chaos-Particle Swarm Optimization
Method is added chaos factor pair population and is initialized, population is made more uniformly to be distributed.Introduce the think of of discussion mechanism and variation
Think, population is enable to jump out locally optimal solution, finally obtain best Butut scheme, not only there is success rate is high, optimizing is fast etc.
Feature, and this layout method based on improved discussion mechanism Chaos particle swarm optimization algorithm can quickly obtain fixed edge
Effective solution of frame floor planning.
Description of the drawings
Fig. 1 is the flow chart of the present invention；
Fig. 2 is the experimental result of the fixed border Butut under depth-width ratio λ=1 of the present invention；
Fig. 3 is the convergence result of the particle cluster algorithm under depth-width ratio λ=1 of the present invention；
Fig. 4 is the experimental result of the fixed border Butut under depth-width ratio λ=2 of the present invention；
Fig. 5 is the convergence result of the particle cluster algorithm under depth-width ratio λ=2 of the present invention；
Fig. 6 is the experimental result of the fixed border Butut under depth-width ratio λ=3 of the present invention；
Fig. 7 is the convergence result of the particle cluster algorithm under depth-width ratio λ=3 of the present invention.
Specific implementation mode
The present invention will be further described with reference to the accompanying drawings and embodiments.
Fig. 1 is please referred to, the present invention provides a kind of based on the VLSI fixed border layout planning methods for improving population, spy
Sign is：
Step S1:Convert fixed border floor planning problem to population optimization；
Step S2:The module of chip is expressed as to the character of corresponding particle, and using the representation of character string sequence
Topological relation between module；
Step S3:The optimal value of the white area requirements of lowest empty must be reached with discussion mechanism Chaos particle swarm optimization algorithm；
Step S4：Optimal floor planning is carried out to entire module according to obtained optimal value.
In an embodiment of the present invention, further, the step S1 is specially：
The object function of fixed border floor planning is defined as
F (F)=A (1)
Wherein：A indicates chip area.
In an embodiment of the present invention, further, the step S2 is specially:If some particle is Pi, then character string
Sequence represents corresponding Butut scheme, each character λ of particle represents a module, can obtain particle PiCoding
For:
Pi=< λ1,λ2, λ3..., λn> (1≤λ≤n, λi≠λj, wherein i ≠ j) and (2)
In an embodiment of the present invention, further, the laying method of the module is:
A. the module representated by particle first character will be placed on the lower left corner of fixed border；
B. he puts at all modules successively according to sequence from left to right；
If c. the remaining space of fixed border not enough places next module, newline puts the module.
During particle is decoded into Butut scheme, multiple positions can be positioned over to solve some module using Greedy strategy
The case where setting, i.e.,:During from left to right putting module, if multiple positions have enough width that can put current block,
Lower position is then preferentially selected to place, to reach the smaller purpose of chip height.
In an embodiment of the present invention, further, logistic changes are introduced to bring to the progress of whole Butut scheme initially
Change, when population optimizing reaches threshold value, randomly selects a Butut scheme every time into row variation and comparison result, it is more excellent, retain
The particle.
In an embodiment of the present invention, further, the improvement particle cluster algorithm is specially：
S31:Whole Butut schemes are initialized, logistic transformation are carried out to first half, μ=4 make all cloth
Figure scheme is in Complete Chaos state, and latter half is generated at random；
S32:Calculate the area of each Butut scheme；
S33:The Butut scheme of single area minimum value is preserved, the Butut scheme of entire area minimum is updated；
S34:According to the Butut scheme that following formula update is whole：
Vi k+1=ω Vi k+c1r1(PLbest-Pi k)+c2r2(PGbest-Pi k) (3)
Pi k+1=Pi k+Vi k+1 (4)
Wherein V indicates that the speed of particle, P indicate that the position of particle, ω are inertia weight, and c1 and c2 are the study more than O
The factor, r1 and r2 are that equally distributed random number, P are obeyed in [0,1] rangeGbestIt is global optimum, PLbestIt is local optimum
Value；
S35:Expand population and mutation process, each step all update PGbestAnd PLbest；
S36:Cycle carries out step S32-235 and obtains the optimal value for meeting preset condition and export result.
In an embodiment of the present invention, further, the step S35 is specially：
A. P is judged to each particleLbestIt repeats whether constant number reaches the upper limit, reaches, carry out mutation operation, compare
As a result, it is more excellent, replace reservation；
B. judge the optimal P of the group of two populationsGbest1 and PGbestWhether 2 numbers of repetition reach the upper limit, reach, and use
The process of initialization expands population scale；
C. to two populations, each population repeats certain number, randomly selects a particle every time into row variation and compare
As a result, it is more excellent, retain；
D. certain number is repeated, a particle is respectively randomly selected from two populations every time and comes out, according to Inew=uI1+
(1-u)I2, in formula：InewThe offspring individual generated for 2 individual fusions； I1And I2Expression receives 2 individuals of mixing operation；u
For the random number between one 0 to 1, along with random perturbation generates new particle, comparison result is more excellent, replaces reservation；
E. to the P of two populationsGbest1 and PGbest2 carry out the fusion process of d steps；
F. certain number is repeated, mutation process is carried out to particle optimal in two populations；
G. to PGbestSubtle adjustment is carried out, uncomfortable whole sequence of modules, only random adjustment module is vertical and horizontal.
In order to allow those skilled in the art to be better understood from technical scheme of the present invention, the present invention is carried out below in conjunction with attached drawing
It is discussed in detail.Embodiment one：
With reference to table 1, using MCNC ami49 reference circuits, experimental setup depth-width ratio is incrementally increased from 1 to 3.5, clear area
Domain ratio is γ=15%, is run 50 times, each iteration 300 times, Population Size range：[25,250], EP (end of program) condition
It is:If obtained Butut result meets depth-width ratio and clear area ratio, algorithm success, and lists algorithm in experiment and obtain
The loop iteration number of satisfactory solution.
Blank area ratio in table 1MCNC ami49 reference circuits under the conditions of different aspect ratios obtains following for satisfactory solution
Ring iterative number and Butut success rate.
With reference to table 1, it can be seen that when given chip depth-width ratio is relatively small, i.e., layout shapes more they tend to square
When, the clear area rate of this paper algorithms is optimal, such as when the ratio of width to height is λ=1, the fixed border of Butut is a pros
Shape, most of operating condition are that program reaches in iterations and can find qualified Butut within 210 times as a result, simultaneously
And obtained Butut outcome quality is all relatively good, and white space is smaller, and minimum is 6.52%, and not more than 9.25%, it is real
It is average to test the quality solved.
With reference to Fig. 2-Fig. 7, the distribution situation of Butut result of the invention and population.The experimental results showed that proposed
Particle cluster algorithm has reliability high, and the fast feature of optimizing, this method can restrain after spending few interative computation, be expired
The feasible solution constrained enough illustrates the method based on particle swarm optimization algorithm fixed border floor planning problem than traditional cloth
Drawing method is good.
The foregoing is merely presently preferred embodiments of the present invention, all equivalent changes done according to scope of the present invention patent with
Modification should all belong to the covering scope of the present invention.
Claims (6)
1. a kind of based on the VLSI fixed border layout planning methods for improving population, it is characterised in that：
Step S1:Convert fixed border floor planning problem to population optimization；
Step S2:The module of chip is expressed as to the character of corresponding particle, and using the representation module of character string sequence
Between topological relation；
Step S3:The optimal value of the white area requirements of lowest empty must be reached with modified particle swarm optiziation；
Step S4：Optimal floor planning is carried out to entire module according to obtained optimal value.
2. according to claim 1 based on the VLSI fixed border layout planning methods for improving population, it is characterised in that：
The step S1 is specially：
The object function of fixed border floor planning is defined as
（1）
Wherein：Indicate chip area.
3. according to claim 2 based on the VLSI fixed border layout planning methods for improving population, it is characterised in that：
The step S2 is specially:If some particle is Pi, then character string sequence represent corresponding Butut scheme, particle each
CharacterA module is represent, particle P can be obtainediBe encoded to:
（2）.
4. according to claim 3 based on the VLSI fixed border layout planning methods for improving population, it is characterised in that：
The laying method of the module is:
A. the module representated by particle first character will be placed on the lower left corner of fixed border；
B. other all modules are put successively according to sequence from left to right；
If c. the remaining space of fixed border not enough places next module, newline puts the module.
5. according to claim 1 based on the VLSI fixed border layout planning methods for improving population, it is characterised in that：
The discussion mechanism Chaos particle swarm optimization algorithm is specially：
S31:Whole Butut schemes are initialized, logistic transformation are carried out to first half, μ=4 make all Butut sides
Case is in Complete Chaos state, and latter half is generated at random；
S32:Calculate the area of each Butut scheme；
S33:The Butut scheme of single area minimum value is preserved, the Butut scheme of entire area minimum is updated；
S34:According to the Butut scheme that following formula update is whole：
（3）
（4）
Wherein V indicates that the speed of particle, P indicate the position of particle, and ω is inertia weight, c1And c2For the Studying factors more than O,
r1And r2To obey equally distributed random number, P in [0,1] rangeGbestIt is global optimum, PLbestIt is local optimum；
S35:Expand population and mutation process, each step all update PGbestAnd PLbest；
S36:Cycle carries out step S32-235 and obtains the optimal value for meeting preset condition and export result.
6. the VLSI fixed border layout planning methods according to claim 5 based on discussion mechanism Chaos-Particle Swarm Optimization,
It is characterized in that：The step S35 is specially：
A. P is judged to each particleLbestIt repeats whether constant number reaches the upper limit, reaches, carry out mutation operation, comparison result,
It is more excellent, replace reservation；
B. judge the optimal P of the group of two populationsGbest1 and PGbestWhether 2 numbers of repetition reach the upper limit, reach then using initialization
Process expand population scale；
C. to two populations, each population repeats certain number, randomly selects a particle every time into row variation and comparison result,
It is more excellent, retain；
D. certain number is repeated, a particle is respectively randomly selected from two populations every time and comes out, according to Inew=uI1+(1-u)
I2, in formula：InewThe offspring individual generated for 2 individual fusions；I1And I2Expression receives 2 individuals of mixing operation；U is one
Random number between 0 to 1, along with random perturbation generates new particle, comparison result is more excellent, replaces reservation；
E. to the P of two populationsGbest1 and PGbest2 carry out the fusion process of d steps；
F. certain number is repeated, mutation process is carried out to particle optimal in two populations；
G. to PGbestSubtle adjustment is carried out, uncomfortable whole sequence of modules, only random adjustment module is vertical and horizontal.
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