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Difference between floorplanning and placement tests

difference between floorplanning and placement tests

The requirement for modular floorplanning and placement that, among different ratios, the delay has at most 38% difference for the the tested cases. We propose to integrate min-cut placement with fixed-outline floor-planning to solve the more general placement problem, which includes cell. Standard-cell layout is shown in Figure 4(a). Table 1: A comparison of common algorithms for partitioning, floor- modules in one partition. This also allows. FOREX TRADING PLAN EXAMPLE

This will minimize the area of the manageable system and the complexity of the large system. When the circuit is partitioned, the connection between two modules or say partitions should be minimum. It is a design task by applying a hierarchical algorithmic approach to solve typical combinatorial optimization problems like dividing a large circuit system into smaller pieces. Figure 1 shows the design flow for the proposed approach. Figure 1 Design flow for the proposed approach.

The method of finding block positions and shapes with minimizing the area objective is referred to as floorplanning. The input to the floorplanning is the output of system partitioning and design entry. Floorplanning paves the way to predict the interconnect delay by estimating the interconnect length.

This is achieved because both interconnect delay and gate delay decrease as feature size of the circuit chips is scaled down—but at different rates. The goals of floorplanning are to a arrange the blocks on a chip, b decide the location of input and output pads, c decide the location and number of the power pads d decide the type of power distribution, and e decide the location and type of clock distribution.

Placement is much more suited to automation than floorplanning. The goal of a placement tool is to arrange all the logic cells with the flexible blocks on a chip. Ideally, the objectives of placement are to a minimize all the critical net delays, b make the chips as dense as possible, c guarantee the router can complete the routing step, d minimize power dissipation, and e minimize cross-talk between signals. The most commonly used objectives are a minimizing the total estimated interconnect length, b meeting the timing requirement for critical nets, and c minimizing the interconnect congestion.

Once the floorplanning of the chip and the logic cells within the flexible blocks placement are completed, then it is time to make the connection by routing the chip. This is still a hard problem that is made easier by dividing into smaller problems. Routing is usually split into global routing followed by the detailed routing. Global routing is not allowed to finalize the connections; instead it just plans the connections to achieve in a better way.

There are two types of areas to global route: one inside the flexible blocks and another between the blocks. The global routing step determines the channels to be used for each interconnect. Using this information the detailed router decides the exact location and layers for each interconnect. The objectives are to minimize the total interconnect length and area and minimize the delay of critical paths [ 2 ]. Figure 15 shows the overall area minimized using hybrid evolutionary algorithm. When the physical design components like partitioning, floorplanning, placement, and routing are combined and optimized in terms of area, then the cost increasing criteria like power and clock speed of each module can be controlled, and these subobjective criteria can also be optimized to a further extent.

In the last three decades many interchanging methods have been used which also resulted in local optimum solutions. And later some of the mathematical approaches were also introduced with some heuristics models which resulted in better result but they have their own advantage and disadvantage. Since lots of solutions are possible for this kind of problem, hence stochastic optimization techniques are commonly utilized.

Till today many techniques have been proposed like global search algorithm GSA which combines the local search algorithm LSA to produce a better result. Global optimization technique like genetic algorithm GA which captured the context of generation from biological system had been used for physical design problems like circuit partitioning, floorplanning, placement, and routing.

Genetic algorithm has been applied to several problems, which are based on graph because the genetic analogy can be most easily applied to any kind of problems. Lots of researchers have proposed their theories to minimize the feature size of the circuit using GA.

Theodore Manikas and James Cain proposed that GA requires more memory but it takes less time than simulated annealing [ 3 ]. Recent enhancements are based on the concept of placement floorplacers may be able to automatically choose an acceptable shape. In particular, we evaluate recent work sistent terminal propagation. This change improves both wirelength on mixed-size placement [6, 24] which relies on greedy legalization and routability. Table 2 compares routability of placements pro- of cell macro locations through left or right packing.

Such strate- duced by three leading min-cut placers on the IBM-Dragon v2 gies typically produce unroutable standard-cell placements [31, 5], benchmarks. We run Dragon 3. This significantly increases routability may be less effective with large circuit blocks present, wirelength, but produces more routable placements.

As of August due to the fragmentation of layout. More generally, it seems that , FengShui [24] does not have such a mode and shifts all cells reliable incremental modification of mixed-size layouts is more dif- to the left or right , typically yielding unroutable placements. Therefore, in this work we attempt to minimize the need for such modification. Section 2 describes A typical floorplanning formulation deals with a set of circuit relevant previous work.

In Section 3 we integrate floorplanning into modules, each characterized by area and shape type. Rectangular partitioning-based placement. The Appendix introduces new mixed- modules blocks may have varying aspect ratios soft blocks. This size placement benchmarks which are used for empirical validation is common for IP blocks available in several shapes, and for hier- in Section 4.

Section 5 concludes our paper. Classical floorplanning minimizes a linear combi- As pointed out in [19, 10, 3], modern hierarchical ASIC design nation of floorplan area and total net length. However, in modern flows are typically based on fixed-die floorplanning, placement and design flows the floorplan often has a fixed outline [19], which ac- routing, rather than the older variable-die style.

In such a flow, each centuates the minimization of wirelength, reminding of placement. Slack represents the amount of hor- Top-down placement algorithms seek to decompose a given place- izontal or vertical space next to each block and can be computed ment instance into smaller instances by sub-dividing the placement quickly. To improve the width of a floorplan, one must relocate a region, assigning modules to subregions and cutting the netlist hy- block with zero horizontal slack similarly for height.

Such moves pergraph [10]. In this context a placement bin represents i a place- are performed at regular time intervals during Simulated Annealing ment region with allowed module locations sites , ii a collection to bias the aspect ratio of the current floorplan to that of the desired of circuit modules to be placed in this region, iii all signal nets outline.

When the temperature schedule runs out, the final floorplan incident to the modules in the region, and iv fixed cells and pins may still violate the outline. The top-down placement process can be viewed as a sequence of passes where each pass examines all bins and divides some of Circuit Capo 9. Most commonly the division step is accom- routed WL Viol routed WL Viol routed WL Viol plished with balanced min-cut partitioning that minimizes the num- ibm01e 0 53 time-out ber of signal nets connecting modules in multiple regions.

These ibm01h 0 time-out techniques leverage well-understood and scalable algorithms for hy- ibm02e 0 0 ibm02h 0 0 time-out pergraph partitioning and typically lead to routable placements. Bins with seven cells or less are processed with an Table 2: Routing results on IBM-Dragon V2 benchmarks with a optimal end-case placer. To allow the partitioners to find better cuts, hour time-out. WarpRoute often fails on FengShui placements.

The top-level coarse netlist of approximately is becoming increasingly important. Much progress has been made clusters is placed using Simulated Annealing, after which the recently [1, 2, 14, 24, 30], and we survey relevant algorithms below. All intermediate cluster with attraction forces and introduces additional repulsion forces be- placements in mPG-MS are non-overlapping, which is enforced with tween overlapping modules.

The new module locations achieved by specially-designed data structures and yet takes considerable com- applying those forces are estimated by solving the Poisson equation, putational effort. This and the pervasive use of Simulated Annealing which is reduced to solving large sparse systems of linear equations.

While mPG finds better placements than those Forces are recomputed for each new placement, and the algorithm reported in [1], even better placements have been produced recently is applied until convergence. Kraftwerk is fast and can success- by the min-cut technique below, which is also much faster. First, the min-cut placer FengShui [6] generates an ini- large modules in realistic circumstances where blocks may be diffi- tial placement for the mixed-size netlist without trying to prevent cult to pack [2].

In a recent empirical comparison of standard-cell all overlaps between modules. The placer only tracks the global placers [5] Kraftwerk was outperformed by several min-cut tools. While giving min-cut partition- clearly discrete optimization problem. The placement engine is a combination of quadratic and tations, not considered in [24] relevant benchmarks use only square min-cut techniques.

It balances partition areas by shifting the cut- blocks with all pins placed in the centers. As described, the algorithm The second stage consists of removing overlaps by a fast legal- assumes pre-determined orientations for all circuit modules and does izer designed to handle large modules along with standard cells.

The not attempt to optimize them. No empirical comparisons to other legalizer is essentially greedy and attempts to shift all modules to- techniques or scalability data are available. It is especially unclear if wards the left edge of the chip or to the right edge, if that produces this technique can handle large, fixed-size, difficult-to-pack blocks. In our experience, the implementation reported in The work in [2] proposes a methodology for mixed-size placement [24] leads to horizontal stacking of modules and sometimes yields that combines floorplanning and standard-cell techniques as follows.

During pre-processing, each large module is shredded are present the benchmarks used in [24] contain numerous modules into small fake cells connected by a grid of fake wires. Pins are of medium size. See Figure 5 for examples of this behavior. An- propagated to shredded cells to reflect pin offsets.

Assigning other concern about packed placements is the harmful effect of such sufficiently high weights to fake wires ensures that fake cells a strategy on routability, explicitly shown in [31]. Overall, the work belonging to the same large module are placed next to each in [24] demonstrates very good legal placements for common bench- other if the placer minimizes linear wirelength.

A black-box marks, but questions remain about the robustness and generality of standard-cell placer is applied to the shredded netlist. We address these Step 2. Initial locations of large modules are computed by aver- questions with additional benchmarking in our work. A module is rotated according to the prevailing orientation in the grid that models it. To remove overlaps between large modules, small cells are clus- 3. In this section we introduce our correct-by-construction approach Step 3.

Non-overlapping locations of large modules are gener- to floorplacement, which does not rely on post-placement legaliza- ated by running a fixed-outline floorplanner, e. Initial locations can be discarded, or else can be re-used with low-temperature annealing during floorplanning. Large modules are fixed, and remaining soft blocks We first observe that min-cut placers scale well in terms of runtime are disintegrated into original standard cells.

The black-box and wirelength minimization, but cannot produce non-overlapping standard-cell placer is called again to re-place small cells. On the other Observe that the shredding process facilitates physical location- hand, annealing-based floorplanners can handle vastly different mod- based clustering of small cells and thus improves final locations ule shapes and sizes, but only for relatively few modules at of large modules, even if their initial locations are discarded.

A a time. Otherwise, either solutions will be poor or optimization will major advantage of this methodology is its robustness — it often take too long to be practical. As explained in Section 2. It also optimizes module orien- ment proposed in [2] suffers from a similar drawback because its tations. This fully-automated methodology successfully competed single top-level floorplanning step may have to operate on numer- with a major commercial tool in and has been recently im- ous modules.

Bottom-up clustering can improve the scalability of proved by more judicious handling of whitespace [1]. Yet, the main annealing, but not sufficiently to make it competitive with other ap- scalability bottleneck remains in the use of Simulated Annealing at proaches. Therefore, in this work we apply min-cut placement as the top-level floorplanning stage. It affects both runtime and the much as possible and delay explicit floorplanning until it becomes quality of wirelength optimization.

Variables: queue of placement bins ing. Backtracking does incur some overhead in failed floorplan runs, Initialize queue with top-level placement bin 1 While queue not empty but this overhead is tolerable because merged bins take considerably 2 Dequeue a bin longer to floorplan.

As the bin may contain numerous standard 9 Undo one partition decision. Merge bin with sibling cells, we reduce the number of movable objects by conglomerating 10 Mark new bin as merged and enqueue standard cells into soft placeable blocks.

This is accomplished by a 11 Else if bin small enough simple bottom-up connectivity-based clustering [22]. The existing 12 Process end case large modules in the bin are usually kept out of this clustering. To 13 Else 14 Bi-partition the bin into smaller bins further simplify floorplanning, we artificially downsize soft blocks 15 Enqueue each child bin consisting of standard cells, as in [1], because standard cells will be placed later anyway.

The clustered netlist is then passed to the ran- Figure 2: Our floorplacement algorithm. Bold-faced lines are domized fixed-outline floorplanner Parquet, which sizes soft blocks different from traditional min-cut placement. We allow at most five attempts to find a non-overlapping placement of modules within the bin.

If We start with a single placement bin representing the entire lay- the floorplanner is successful, the locations of all large modules are out region with all the placeable objects initialized at the center of returned to the top-down placer and considered fixed.

The rows be- the placement bin. Using min-cut partitioning, the bin is split into low those modules are fractured and their sites are removed, i. At this point, min-cut justed according to actial partition sizes. Applying this technique placement resumes with a bin that has no large modules in it, but recursively to bins with terminal propagation produces a series of has somewhat non-uniform row structure.

When min-cut placement gradually refined slicing floorplans of the entire layout region, where is finished, large modules do not overlap by construction, but small each room corresponds to a bin. However, this area. Those overlaps are quickly detected and removed with lo- scheme breaks down on modules that are greater than their bins. Detailed placement uses branch-and-bound placement in slid- continue, or else will likely produce a placement with overlapping ing windows [8], but does not move the macros.

Figure 1 b shows modules. Indeed, the work in [24] continues bisection and resolves a sample placement produced by our tool. This is done for two main reasons: 1 to Empirical boundary between placement and floorplanning. By preserve wirelength [9], congestion [7] and delay [20] estimates that identifying the characteristics of placement bins for which our al- may have been performed early during top-down placement, and 2 gorithm calls floorplanning, one can tabulate the empirical bound- avoid the need to legalize a placement with overlapping macros.

In ary between placement and floorplanning. Formulating such ad hoc particular, we are unconvinced that existing legalization algorithms thresholds in terms of dimensions of the largest module in the bin, are robust enough to handle a wide variety of module shapes and etc allows one to avoid unnecessary backtracking and decrease the sizes in realistic netlists see Figure 5. We also anticipate difficulty overhead of floorplanning calls that fail because they are issued too ensuring routability while shifting macros and standard cells at the late.

In practice, issuing floorplanning calls too early i. To improve While resorting to fixed-outline floorplanning is a natural step, wirelength, our ad hoc tests for large blocks in bins that trigger successful fixed-outline floorplanners have appeared only recently floorplanning are deliberately conservative. This bin is floorplanned, most one level of backtracking block-merging is required to pre- and in the case of failure can be merged with its sibling again.

The vent overlaps between large modules. Side-effect: Narrow vertical slivers between large modules. Ad- It is typically easier to satisfy the outline of a merged bin because jacent large modules placed by the fixed-outline floorplanner may circuit modules become relatively smaller.

However, Simulated An- have tall, narrow columns of empty sites between them. Fitting small nealing takes longer on larger bins and is less successful in mini- cells in such slivers may be non-trivial, e. Therefore, it is important to floorplan at just the four sites and a collection of cells that take two or three sites each. Therefore, a traditional min-cut placer that assigns cells to bins bisection generates a grid-like floorplan. Since our floorplacer includes a state- benchmark.

Our floorplacer determines both locations and shapes of-the-art floorplanner [3], it can natively handle pure block-based of individual modules to minimize wirelength. Traditional rectangu- designs. Unlike most algorithms designed for mixed-size place- lar floorplanning with Parquet is compared to our free-shape non- ment, it can pack blocks into a tight outline, optimize block orienta- rectangular floorplacement on the right.

Indeed, when the number of blocks is very small, our algorithm applies floorplanning right away. An addi- However, when given a larger design, it may start with partition- tional benefit of our approach is its scalability, e. This are present, everything is accomplished without Simulated Anneal- is demonstrated in Figure 1 a which shows the block-based design ing.

Figure 3 b reports the improvement in runtime and wirelength n placed using our floorplacer. The cuts made by the min-cut over traditional rectangular floorplanning with Parquet on a mix of partitioner are clearly seen making the resulting floorplan globally MCNC and GSRC floorplanning benchmarks. For larger designs, slicing, but locally non-slicing. We expect that this new and is more successful at minimizing wirelength than annealing- type of free-shape floorplanning can be useful before logic synthe- based floorplanning, the proposed approach is scalable and effective sis to determine relative locations of large modules and enable early at minimizing wirelength.

This expectation is fully confirmed by estimates of signal delays in global interconnect. Free-shape rectilinear floorplanning. Some circuit modules, such 4. However, when only the area of a module is esti- posed floorplacer in large-scale congestion-driven standard cell place- mated, but its shape is unknown, there is often no a priori reason ment and free-shape floorplacement. Below we validate our tool on to limit its shape to rectangles. Such limitations may be justified by designs with hard blocks and on mixed-size placement instances.

Non-rectangular floorplanning has been 4. Com- type of modules. To this end, the work in [23] and [29] repre- parisons of other floorplanners to Parquet can be found in recent sents simple non-rectangular shapes with Sequence Pairs SP and literature on floorplanning. We first convert the benchmarks to the Bounded Slicing Grids BSG to pack such modules using the popu- GSRC bookshelf format for placement using an internal converter lar annealing-based framework.

Since specific floorplanning formulation proposed in [19], which assumes area minimization is not an objective as long as we fit within the desired locations of given rectangular modules and seeks to re-shape fixed-outline constraints, we only report half-perimeter wirelength the modules so as to avoid overlaps. The proposed algorithm is an HPWL and runtimes. For the smallest three benchmarks n10, incremental detailed floorplanner that tends to generate fairly com- n30 and n50 the two approaches perform similarly, as the floor- plicated shapes, but does not account for interconnect.

Below we placer resorts to floorplanning. However, the larger the designs, the extend our global free-shape floorplanner to generate both locations more partitioning calls are made by the floorplacer. This results in and shapes of soft modules so as to minimize interconnect. Empir- faster and more powerful interconnect optimization compared to the ically, most of the modules are shaped as rectangles, but L-,T- and annealing-based Parquet tool.

The improvements should be even U-shapes are sometimes created when this helps reducing intercon- more pronounced for larger block-based designs. Our algorithm is also capable of pin placement. Below we rely on techniques proposed in [2], where each large 4. To validate the routability of placements nets. Signal pins of a module are propagated to respective fake produced by Capo 9. However, in our context there is no need to shred fixed-shape the Appendix.

We compare our approach with Cadence Qplace part blocks because they are already handled by our floorplacer. Thus, we only shred soft blocks. However, since we now allow non-rectangular shapes, sec sec Levels there is no need to average locations of fake cells and determine the n10 10 5.

We simply accept module shapes as- n30 30 Because of the relative rigid- n50 50 Other shapes are generated only when this reduces interconnect, and they remain relatively simple.

Difference between floorplanning and placement tests merriam mac williams forex difference between floorplanning and placement tests


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If AR is other than 1 it signify the rectangular shape. The aspect ratio effects the routing resources available in the design. It effects the congestion. Core Utilization: Core Utilization defines the area occupied by standard cell, macros and blockages. If core utilization of 0. Pin Placement is an important step in floorplanning. Pin placement can be done based on timing, congestion and utilization of the chip.

To place IO pads we use a script to place them. If we are doing a digital block, we will need to place pins around the boundary to connect to the higher level routing. Macros may be memory block, analog blocks. Macro placement can be manual or automatic.

Manual macro placement is more efficient when there are few macros to be placed. Automatic macro placement is more appropriate if number of macros is large. Depending on how the macros are placed, the tool places the standard cells in the core. All macros should be placed at the boundary. Check the orientation and pin directions of all macros.

Types of macros Hard macros: Hard macro is a block that is generated in a methodology other than place and route and is imported into GDSII file. Hard macros are block level designs which are optimized for power, area and timing. While accomplishing physical design it is possible to only access pins of hard macros. Soft macros: Soft macros are synthesizble RTL form. Soft macros are editable and can contain standard cell and other soft macros.

Macro placement Guidelines Use flylines and make sure you place blocks that connects to each other closer. If hard macros connect to IO, place them near the respective IO. Spacing between two macros. Provide a halo space around all sides of the macros. Keep placement blockages at the corners of macros.

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Floorplanning - Physical Design - Back To Basics

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