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.ps 14
\fBA New Algorithm for Standard Cell Global Routing\fR
.sp 1
.ps 10
\fIJingsheng Cong\**
.sp 0.25v
Department of Computer Science
University of Illinois at Urbana-Champaign
Urbana, Illinois 61801
.sp 0.5v
Bryan Preas
.sp 0.25v
Xerox Palo Alto Research Center
3333 Coyote Hill Road, Palo Alto, California 94304
\fR
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.(f
\** This work was performed when this author was with Xerox PARC during the summer of 1987.
.)f
.uh Abstract
.pp
In this paper, we present a new algorithm for
standard cell global routing.
The algorithm considers all of the interconnection nets in parallel;
this produces superior results since information about all of the nets is available
throughout the global routing process.
We formulate the global routing as finding the optimal spanning forest (a generalization of optimal spanning trees) on a graph that contains all of the interconnection information. The results of several theorems allow us to prune many non-optimal
connections before the process begins.
This approach successfully
solves the net ordering and
congestion prediction problems which other
approaches suffer. The new algorithm was
implemented as part of the DATools
system at Xerox PARC.
The benchmarks from the Physical Design Workshop are used as part of the comparison suite.
The new algorithm achieves up to
11% area reduction compared to the
previous global routing package used in
the DATools system and
obtains up to 17% reduction in the total channel
density compared to the Timberwolf 4.2 package.
In no case does the new algorithm do worse than its competitors.
.sp 1
.sh 1 Introduction
.pp
The standard cell design style is widely used in the design of VLSI circuits; the
cells (either obtained from a library or constructed) are arranged in horizontal rows.
Due to the inherent complexity of physical design, the
process is usually divided into three steps: placement, global
routing and channel routing. During placement,
we determine the row and the
position within that row for each cell in the logic design.
Next, during global routing, we determine the connection pattern, or topology, for each net.
Global routing adds \fIfeedthrough\fP cells to make connections across the cell rows,
selects the pins (from the pins connected within the cells) to connect with wiring,
and determines the routing channels in which the wiring should lie.
Then, each channel is routed as a separate channel routing problem.
A significant
amount of work has been done on channel routing. There are several
good channel routing algorithms which can consistently produce routing solutions that are no more than a few tracks above channel density.
Also, modern placement programs
can generate high quality placements for
standard cell designs. However, we see only limited progress in standard
cell global routing. Most global routing discussions are
based on general cell design models.
.pp
The global router in the Highland system [Ro84] was used as the benchmark for standard
cell global routing at the Physical Design Workshop on Placement and Floorplanning [Pr87].
It builds a minimum spanning tree for each net.
The cost for each net edge is a function of channel density, feedthrough
availability and wire length.
An optimization step follows to try to improve the solution. Supowit [Su83] gives an \fIodd-even\fP heuristic algorithm
for standard cell global routing.
It produces a solution within a factor of 1.5 of the optimal
solution, but his result
applies only to problems in which all the nets have only two pins.
Another global router developed for
standard cell designs is part of the Timberwolf package [SeSa86].
It first connects each net by a minimum spanning tree based on wire lengths.
Then it uses iteration (simulated annealing with t = 0) to improve the assignment of net
segments to channels.
The previous standard cell global router [Pr86] used in the DATools system
at Xerox PARC [BaMS88] generates the minimum number of
feedthroughs.
Feedthroughs are added only to connect the nets.
The feedthroughs are then placed, along with the other cells,
as part of a row-based placement improvement step.
More recent work on standard cell global routing was done by Mowchenko and Ma [MoMa87]; they
generalized the left edge algorithm for channel routing to
global routing.
In [AoKu83] and [LiSS86], a similar problem is addressed for
gate array designs, where the objective is to minimize
the maximum channel density.
.pp
A careful study shows that these approaches face one or more
of the following problems:
.ip (1)
The final solutions produced
are sensitive to the order in which the nets are considered
because the nets are connected one by one. However,
little is known about what is a good net order. (In
[MoMa87], they avoided net ordering problems by processing
channel by channel. But still, it is difficult to choose a
good channel order.)
.ip (2)
It is very difficult for these
global routers to predict congested areas in channels when
they add net segments. This is especially true in the early
stages when most net segments have not been included.
.ip (3)
The net connection patterns that can be produced are re\%stricted by the algorithm.
Only a few predetermined topologies are allowed.
.pp
In this paper, we present a new algorithm
for standard cell global routing which successfully overcomes
the problems mentioned above. This algorithm processes all the
nets in parallel, so the results are
independent of the order in which the nets are considered.
Furthermore, better results are produced since information about all of the nets is available
throughout the global routing process.
We introduce the net connection graph
and formulate the problem as finding an optimal spanning forest
of the net connection graph.
According to a theorem in Section 3.2, we can simplify the net connection graph
by pruning a large number of non-optimal connections. This enables our
algorithm to consider all the possible optimal connections in
all the channels. Thus, our algorithm can predict very accurately the
densest areas in each channel and, therefore, distribute
density evenly over all channels to minimize the \fItotal channel density\fP.
(Total channel density is the sum of the channel density over all of the channels.)
.pp
The remainder of this paper describes the algorithm.
Section 2 defines the problem that is being solved and the two-stage algorithm is discussed in Section 3. The computational complexity of the algorithm is summarized in Section 4, and comparative results are presented in Section 5.
The new algorithm gives 6% to 11% better results than the global routing algorithm that it replaced and 5% to 17% better results than that produced by the Timberwolf 4.2 global router.
.sh 1 "Formulation of the Problem"
.pp
The goal of global routing is to determine the connection
pattern for each net and achieve the best utilization of
chip area. The connection pattern is defined by positions for feedthroughs,
the pins to be connected,
and channels in which the net segments that connect the pins lie.
\fIChip area\fR is equal to the product of the width of
the chip and the height of the chip, where the \fIwidth of the chip\fR
is the maximum length (including any feedthroughs) of any row of the chip,
and the \fIheight of the chip\fR
is the sum of the heights of all cell rows and the total channel
density times the line-to-line spacing.
.pp
We define the \fInet connection graph\fR
to be an undirected graph $G~=~(V , ~E )$, where $V $ is the set
of pins currently in the design. An edge
$(p sub i , ~p sub j )$ is in $E $ if
the two pins $p sub i$ and $p sub j$ are in the
same net.
We also call $(p sub i , ~ p sub j ) $ a \fInet segment\fR
if $p sub i$ and $p sub j$ are in the same channel.
Clearly, each net in the net connection graph is a \fPconnected component\fR
and is represented by a complete graph on the pins of the net.
Note that $V$ may grow as we perform the
global routing because new pins are introduced when
feedthroughs are added. A \fIglobal routing solution\fR
is a
spanning forest of the net connection graph.
A spanning forest which yields the minimum chip area
is called an \fIoptimal spanning forest\fR.
.pp
There are two problems involved in standard cell
global routing. The first problem is to determine whether
and where to add feedthroughs. Generally speaking,
feedthroughs have two functions. One function is to complete
the connections among pins that make up the nets.
For the example shown in Figure 2-1(a), we have to insert a
feedthrough in row 2 to complete the connections for the
net as shown in Figure 2-1(b).
.(z
./".PS < fig2-1
.sp 2.8i
.lp
\fBFigure 2-1\fP A feedthrough allows a net to cross a cell row.
The feedthrough in row 2 is required to complete the connection.
.sp 0.5v
.)z
The other function of feedthroughs is to reduce the total channel density.
Consider the net shown in
Figure 2-2(a). Although we can complete the connection without
.(z
./".PS < fig2-2
.sp 2.8i
.lp
\fBFigure 2-2\fP Feedthroughs can also be used to reduce the total channel density.
.)z
adding any feedthroughs, by adding a feedthrough in
row 2 we save a long wire in channel 2.
This may reduce the total channel density.
The second problem in standard cell global routing is to determine the net segments
to complete the connection of the nets
after feedthroughs have been added.
Figure 2-3 shows two different choices of net segments to connect a net.
.(z
./".PS < fig2-3
.sp 2.8i
.lp
\fBFigure 2-3\fP For the net shown, two different choices of net segments are available in the channel between rows 2 and 3.
.sp 0.5v
.)z
At this stage, since the width of the chip is fixed,
the problem is to
build a spanning forest within
the net connection graph to minimize the total channel density.
.pp
In [Su83] and [MoMa87], they assume that all the feedthroughs
have been added and only address the
second problem: determining the net segments. [SeSa86] and [Pr86] use
a simple method to determine feedthrough locations
to complete the connections. Then they concentrate
on the second problem to determine the net segments.
.pp
Solutions to the global routing problem are very difficult; we can show the problem of finding an optimal spanning forest
is NP-hard. In fact, the problem of determining net segments itself
is already NP-hard (even assuming no feedthroughs need to be added).
The NP-hardness result even holds for more restricted subcases. It
is shown in [Ka86] that the problem of determining net segments is still
NP-hard if each net has only two logical pins. We also show that
the problem of determining net segments is NP-hard even if there
are only two channels (see [CoPr88]).
.pp
In the rest of this paper,
we present an efficient algorithm which constructs two minimum
weighted spanning forests in two stages to approximate the optimal
spanning forest.
.sh 1 "The Standard Cell Global Routing Algorithm"
.pp
Our global router works in two stages. In
the first stage, we determine all the feedthroughs
to be added and determine their locations within the rows.
In the second stage, we choose which physical pins will be connected
(and thus choose the net segments) to complete connections for each net.
.sh 2 "Determine Feedthroughs"
.pp
In most previous algorithms, feedthroughs
are added only when connections for some nets cannot be completed.
In our algorithm, we use feedthroughs not only to complete the
connections but also to trade off
the width and height of the chip.
Additional feedthroughs can often reduce track density
and thus reduce chip height with no expense in width.
An additional feedthrough on other than the longest row will not increase the width of the chip.
.pp
We generalize Kruskal's minimum spanning
tree algorithm [AhHU74] to build a minimum spanning forest of
the net connection graph. Feedthroughs are
determined by the intersections of cell rows and the edges in the
minimum weighted spanning forest.
.pp
We weight each edge according to the length of the edge
and the cost to insert feedthroughs for the edge.
For each edge $e~=~(p sub i ,~p sub j )$ in the
net connection graph connecting
two pins $p sub i$ and $p sub j$ in the same net,
we define the weight of $e$
.EQ
w(e)~=~\(br~x sub i ~-~x sub j~\(br~+~K cdot
sum from {e inter R sub i != phi } ~weight(R sub i )
.EN
where $x sub i$ and $x sub j$ are the horizontal
coordinates for $p sub i$ and $p sub j$, respectively.
$K$ is a constant factor. $e inter R sub i ~!=~ phi $ means
net edges $e$ which intersect row $R sub i$.
$weight(R sub i )$ is the weight of row $R sub i$, which
is based on the current length of
row $R sub i$.
Assume we choose $e$ to be included
in the minimum weighted spanning forest. If $p sub i$ and
$p sub j$ are in the same channel, we simply add $e$
into the solution.
If $p sub i$ and $p sub j$
are in different channels, we add feedthroughs
$f sub 1 ,~f sub 2 ,~...~,~f sub l$ in rows
$R sub i sub 1 , ~R sub i sub 2 ,~...~, ~R sub i sub l$
which intersect with $e$. Then we add the path from
$p sub i$ to $p sub j$ through these feedthroughs into the
solution.
The cost
of an edge thus defined is a function of both wire length
and the cost of adding feedthroughs. By assigning
different weights to different rows, we
discourage adding feedthroughs on the longest rows
since this will increase the width of the chip.
On the other hand, when two pins are far apart we
may connect them
to nearby pins in the same net,
even at the cost of extra feedthroughs. These extra feedthroughs may decrease
the total channel density and thus decrease the height of the chip.
We adjust the factor $K$ to control the
trade-off between the width and the height of a chip.
.pp
Note that the weight of each net edge is not static.
After a feedthrough is added, some pin locations and the
weights of some rows may change. If we construct
the minimum spanning forest by building a minimum weighted spanning
tree
net by net, a different order for considering the nets will quite likely lead
to a different result. However,
there is no
efficient algorithm available to determine an
optimal net order. A key insight allows us to overcome this difficulty.
Kruskal's minimum spanning tree algorithm [AhHU74]
can be generalized to build the minimum spanning forest.
Thus, instead of building minimum spanning trees net
by net, our algorithm builds a minimum spanning forest
directly by considering all of the nets in parallel as shown in Algorithm 3-1.
In this algorithm, \fIF\fP represents the minimum spanning forest.
.(b
.nf
1. $E$ := all the edges in the net connection graph;
$F$ := $phi$;
2. \fBwhile\fR $E$ <> $phi$ \fBdo\fR
remove the minimum weighted edge $e$ from $E$;
\fBif\fR $F~union~"{"e"}"$ does not have a cycle
\fBthen\fR include $e$ (or the path induced by $e$) in $F$;
update $E$ if feedthroughs are added;
\fBend-while\fR
3. \fRoutput\fR $F$
.fi
.lp
\fBAlgorithm 3-1\fP Determine Feedthroughs (Stage 1 of Global Routing)
.sp 0.5v
.)b
The advantages of this algorithm are clear. Since we consider the edges of all nets
at the same time, the algorithm is independent
of input net order. The spanning tree for each
net gets an equal chance to grow. Information about all nets is available throughout the process.
.pp
It is necessary to show that this algorithm will converge since
we keep adding new vertices (induced by feedthroughs) to the net
connection graph. In [Ro84], a limit is set on the
total number of vertices allowed for each net. However,
for our algorithm we can show that $\(br~V~\(br~-~\(br~F~\(br$
decreases by one each time an edge (or a path) is
added to $F$. ($V$ is the set of vertices in the net connection
graph and $F$ is the set of edges in the partially constructed spanning forest.)
Based on this observation, we have
.pp
\fBTheorem 3-1\fR The Stage 1 algorithm will converge to a minimum
spanning forest after $n~-~1$ steps of inclusion, where $n$
is the total number of pins originally in the design.
.sh 2 "Determine Net Segments"
.pp
After Stage 1, all of the required feedthroughs have been added
and their positions have been determined; we will not
add new feedthroughs. Thus, we can remove those edges
that cross the cell rows (but are not \fIbuilt-in\fP edges
that represent feedthroughs or connections within the cells) from the net connection graph.
The solution of Stage 2 is a spanning forest $S$ within the
net connection graph such that each edge in $S$ lies entirely
in one channel. Since the width of a chip is fixed after
Stage 1, the objective in Stage 2 is to minimize the height of the chip by minimizing the total
channel density.
.pp
Most previous approaches to global routing build a spanning tree
for each net one by one. And for each net, these algorithms keep
adding the minimum weighted
feasible edge until a spanning tree is obtained.
A serious problem exists with these approaches. It is very difficult to decide whether a net segment should be added to
a channel since these algorithms have no knowledge of the density of the channel
in the final solution, especially
early in the execution of the algorithms when only
few net segments are present.
Also, these approaches
face the problem of choosing a good order in which to
process the nets.
.pp
To avoid these problems, we define a new algorithm.
First, the basic approach is described; then we will show the performance improvements and refinements.
The algorithm constructs a minimum weighted spanning
forest to approximate the optimal spanning forest.
We define the weight of an edge $e$ to be
$w(e)~=~d(e)/d$, where $d(e)$ is the maximum density over
the edge in the channel to which $e$ belongs, and $d$ is the density
of the channel.
First,
we put all the edges in the net connection graph
into an edge set, $S$.
Then we repeatedly remove
the maximum weighted edge
from $S$ as long as we do
not disconnect any net. We update the
weights of edges in a channel whenever an edge is removed
from that channel. The process terminates when
$S$ is a spanning forest.
Clearly, this approach has two advantages:
.ip (1)
Since all the edges are considered from the start,
the algorithm has global information and knows where the most congested
areas are. The weight of each edge
during the construction reflects
the density over that edge
in the resulting spanning forest;
.ip (2)
Since we process all nets in parallel, the result of our algorithm does not depend
on the order in which nets are processed.
.pp
The following theorem shows that the
result is close to the optimal spanning forest:
.pp
\fBTheorem 3-2\fR Let $S$ be a set of edges in the net connection graph.
Let $C(S)$ be the subset of edges in $S$ such that each
edge in $C(S)$ lies on a
cycle in $S$. The edge weights are equal to the density of
the channel to which the edge belongs. We have
.ip (1)
If $C(S)~!=~ phi$, then there exists an edge $e$ in
$C(S)$ such that $S$ contains an optimal spanning forest
iff $S~-~"{"~e~"}"$ contains an optimal spanning forest;
.ip (2)
If $C(S)~=~ phi $, then the optimal spanning forest
based on $S$ yields the same total channel density as
$S$ does.
.pp
For the proof, see [CoPr88]. This theorem justifies that
there is a good chance the spanning forest constructed
by our algorithm is an optimal spanning forest (remember
that computing an optimal spanning forest deterministically
is NP-hard).
.pp
We now describe the refinements to the basic algorithm. Remember there may be $THETA (n sup 2 )$ edges
in the net connection graph at the beginning of Stage 2
(although we removed edges, other than the built-in edges or feedthroughs, which crossed cell rows), where
$n$ is the number of pins after Stage 1.
A straightforward implementation of the above algorithm
suffers two problems.
First, since there are only $O(n)$ edges in the final spanning
forest,
we have to go through $ THETA (n sup 2 )$ steps of edge deletion; this is quite time-consuming. Moreover,
since most of the edges initially in
$S$ are to be removed, the weight of an edge at
the beginning may not closely approximate the channel
density over that edge in the final spanning forest.
A careful study showed that we can overcome these two problems by simplifying the net connection graph before
we compute the spanning forest
$S$.
We define the \fIsimplified net connection graph\fR $SG$ to be an
undirected graph, whose vertex set is the set of pins in
the design, and for two pins $p sub i$ and $p sub j$ of the same net,
$(p sub i ,~p sub j )$ is an edge
of $SG$ iff they satisfy
one of the following conditions: (1) $p sub i$ and $p sub j$ are
on the same cell or feedthrough and are connected internally; or (2)
$p sub i$ and $p sub j$ are in the same channel and have no other
pins of the same net between them. Figure 3-1 shows the example
.(z
./".PS < fig3-1
.sp 1.7i
.lp
\fBFigure 3-1\fP The connected component corresponding to a net in the simplified net connection graph is much more sparse than a complete graph.
The optimal spanning forest is not lost by this simplification.
.)z
of a connected component induced by a net in the simplified net connection graph.
Note it is much more sparse than a complete graph.
In fact, we make the following claims:
.pp
\fBTheorem 3-3\fR Let $n$ and $m$ be the number
of vertices and edges, respectively, in the simplified net connection graph. Then
.ip (1)
$m ~<=~1.5 n$.
.ip (2)
The simplified net connection graph can be
constructed in $O(n log n)$ time, where $n$ is the total number of
pins in the design.
.ip (3)
The simplified net connection graph contains
an optimal spanning forest.
.pp
For the proof, see [CoPr88]. The benefits from the theorem are clear.
Since we only have to go through
approximately $0.5n$ edge deletions, our algorithm runs much faster.
Also, since only a relatively
small number of edges in $SG$ are to be removed, the
weight of each edge more accurately measures the density
over the edge in the resulted spanning forest. Moreover,
we can compute the simplified net connection graph
efficiently without losing the optimal spanning forest.
We summarize our algorithm for Stage 2 as follows:
.(b
.nf
1. build the simplified net connection graph;
2. $S$ := all the edges in the simplified net connection graph;
3. \fBrepeat\fR
Remove the maximum weighted edge in $S$ on a cycle;
Update edge weights for the affected edges;
\fBuntil\fR $S$ is a spanning forest;
4. output $S$.
.sp 0.5v
.fi
.lp
\fBAlgorithm 3-2\fP Determine the Net Segments (Stage 2 of Global Routing)
.)b
.sh 1 "Complexity"
.pp
Let $n$ be the total number of physical pins originally in the design.
(In modern standard cells, there are usually two physical pins per logical pin.)
Let $p$ be the average number of (physical) pins
per net. Typical values for $p$ range between 4 and 8.
Based on Theorems 3-1 and 3-3, the overall time complexity of our new algorithm for
standard cell global routing is
$O(n sup 2~max (p,~ log n))$. For details of the analysis, see
[CoPr88].
.sh 1 "Experimental Results"
.pp
We implemented our algorithm in the Cedar language running on
Xerox Dorado workstations (2-MIPS machines) and incorporated it into the DATools system
developed at Xerox PARC.
Table 5-1 summarizes the examples used to compare the new algorithm with the previous global router in the DATools system and with the global router in Timberwolf 4.2.
The counters and adders are the circuits synthesized by the DATools system
when no performance requirements are imposed. Both types of circuits are simple, ripple-carry designs.
Primary 1 and Primary 2 are the benchmarks from the Physical Design Workshop [Pr87].
.(z
.TS
center, allbox;
c c c c c
l n n n n.
Ex. # cells # IOs # nets # pins
16-b adder 144 50 177 546
16-b ctr 173 56 206 609
32-b adder 288 98 355 1090
32-b ctr 342 104 396 1203
64-b adder 576 194 707 2178
64-b ctr 681 200 783 2393
Primary 1 752 81 904 2737
Primary 2 2907 107 3029 8758
.TE
.lp
\fBTable 5-1\fP A summary of the example circuits used to compare the new algorithm with the previous algorithm and with the global router in Timberwolf.
.)z
.pp
Table 5-2 summarizes the experiments comparing the old and new algorithms.
Compared to the previous standard
cell global routing package used in the DATools system, the new algorithm
achieves a 6% to 11% area reduction.
In general, more feedthroughs are added by the new algorithm, but the chip widths are not increased.
The extra feedthroughs are being used to reduce chip height by reducing total track density.
.pp
.(z
.TS
center, box;
c| c | c s | c s | c
c| c | c c | c c | c
c| c | c|c | c|c | c
l| n | n|n | n|n | n.
Ex. # of previous algo. new algo. sav-
\↑ \↑ ← ← ← ← \↑
\↑ rows \s-2width\s+2 \s-2height\s+2 \s-2width\s+2 \s-2height\s+2 ing
←
16-b adder 4 1104 812 1104 764 6.3%
←
16-b ctr 5 1320 1120 1320 1008 11.1%
←
32-b adder 6 1528 1415 1528 1324 6.8%
←
32-b ctr 7 1904 1736 1904 1624 8.5%
←
64-b adder 8 2448 1956 2448 1860 5.1%
←
64-b ctr 9 3096 2744 3096 2456 11.7%
.TE
.lp
\fBTable 5-2\fP Comparisons with the previous global routing package in the Xerox PARC DATools system.
.)z
.pp
We also compared our global routing
results with the results produced by the Timberwolf 4.2 global routing on the Physical Design Workshop benchmarks.
In both examples, the global routing is performed on the same placement (the one produced by Timberwolf). Table 5-3 shows the comparisons on these examples. We obtained 5% to 17% reduction in total track density and over 20% reduction in the number of
inserted feedthroughs compared to Timberwolf.
.pp
Our global router is a straightforward implementation of the algorithm described here. As a result, there are a number of opportunities to significantly improve the results. Some standard cell global routing packages improve the global routing (and further reduce area) by
exchanging adjacent cells in the same row or modifying the cell orientation ([Ro84, SeSa86, Pr86]). In addition, the cells in the Physical Design Workshop Benchmarks (Primary 1 and Primary 2) have a large number of built-in feedthroughs that are exploited by Timberwolf, but not by the global router described here. (The standard cells used at Xerox PARC do not have built-in feedthroughs so this feature was not included.)
We did not implement these refinements in the current version of
our global routing package.
.(b
.TS
center, box;
c| c | c s | c s | c s
c| c | c c | c c | c c
c| c | c|c | c|c | c|c
l| c | n|n | n|n | n|n.
Ex. # of Timberwolf new algo. improvement
\↑ \↑ ← ← ← ← ← ← \↑
\↑ rows FTs trks FTs trks FTs trks
←
P1 17 1380 223 1120 190 23.2% 17.4%
←
P2 29 4621 474 3761 449 22.9% 5.6%
.TE
.lp
\fBTable 5-3\fP Comparisons with the global routing algorithm in Timberwolf 4.2 [SeSa86] on Primary 1 and Primary 2 benchmarks from the Physical Design Workshop.
.)b
.sh 1 "Remarks and Conclusions"
.pp
In this paper, we present a new algorithm for standard
cell global routing. By processing all the nets in parallel,
we avoid the problems associated with net ordering
and the problems created by lack of information early in the global routing process.
By simplifying the net connection
graph and applying an iterative deletion algorithm for building
spanning trees, we can more accurately predict congested
areas. This global routing algorithm produces high quality solutions
in polynomial time.
.pp
In both Stage 1 and Stage 2, the weights for net edges are
dynamic. A significant amount of time is spent on updating
edge weights in our current implementation. We are working
on designing and implementing more efficient data structures
to speed up the re-computation for edge weights.
.uh References
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