Wire Length Prediction-based Technology Mapping and Fanout Optimization - PowerPoint PPT Presentation

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Wire Length Prediction-based Technology Mapping and Fanout Optimization

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Wire Length Prediction-based Technology Mapping and Fanout Optimization Qinghua Liu Malgorzata Marek-Sadowska VLSI Design Automation Lab UC-Santa Barbara – PowerPoint PPT presentation

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Title: Wire Length Prediction-based Technology Mapping and Fanout Optimization


1
Wire Length Prediction-based Technology Mapping
and Fanout Optimization
  • Qinghua Liu
  • Malgorzata Marek-Sadowska
  • VLSI Design Automation Lab
  • UC-Santa Barbara

2
Outline
  • Motivation and previous work
  • Pre-layout wire length prediction
  • Technology mapping with wire-length prediction
  • Fanout optimization with wire-length prediction
  • Experimental results
  • Conclusions and future work

3
Motivation
  • Traditional logic synthesis does not consider
    accurate layout information
  • Placement quality depends on
  • netlist structure
  • placement algorithm

4
Previous work
  • Logic and physical co-synthesis
  • Layout-driven logic synthesis
  • Local netlist transformations
  • Metric-driven structural logic synthesis
  • Adhesion
  • Distance

5
Pre-layout wire-length prediction
  • Previous work
  • Statistical wire-length prediction
  • Lou Sheffer et al. Why Interconnect Prediction
    Doesnt work? SLIP00
  • Individual wire-length prediction
  • Qinghua Liu et al. Wire Length Prediction in
    Constraint Driven Placement SLIP03
  • Semi-individual wire-length prediction
  • Predict that nets have a tendency to be long or
    short
  • Qinghua Liu et al. Pre-layout Wire Length and
    Congestion Estimation DAC04

6
Summary of the semi-individual wire length
prediction technique
  • Predict lengths of connections
  • Mutual contraction
  • Predict lengths of multi-pin nets by
  • Net range

7
Mutual contraction
B.Hu and M.Marek-Sadowska, Wire length
prediction based clustering and its application
in placement DAC03
v
y
u
x
8
Relative weight of a connection
v
Wr(u, v) 0.71
u
EQ1
y
EQ2
Wr(x, y) 0.5
x
9
Mutual contraction of a connection
Cp(x, y) Wr(x, y) Wr(y, x)
EQ3
Wr(u, v) 0.71 Wr(v, u) 0.33 Cp(u, v) 0.234
Wr(x, y) 0.71 Wr(y, x) 0.6 Cp(x, y) 0.426
y
v
j
i
x
u
10
Predictions on connections
(a)
(b)
Mutual contraction vs. Connection length
11
Net range
0 1 2 3 4 5 6 7
8 9 10 11
Circuit depth
Example of net range
12
Predictions on multi-pin nets
Net range vs. average length for multi-pin nets
13
Technology mapping with wire-length prediction
(WP-Map)
  • Node Decomposition
  • Technology Mapping

14
Node decomposition
a
b
c
G
a
b
c
a
b
c
T.Kutzschebauch and L.Stok, Congestion aware
layout driven logic synthesis, ICCAD01
15
Greedy node decomposition algorithm
CurrentPinNumn
N
CurrentPinNum CurrentPinNum-1
Done
Y
(n1,n2)two input nets with largest mutual
contraction
Update mutual contraction
Decompose(G,n1,n2)
Remove n1 and n2, insert new net
Decompose n-input gate G with wire length
prediction
16
Correlation between mutual contraction and
interconnection complexity
Average mutual contraction vs. Rents exponent
17
Technology mapping
EQ4
18
Fanout optimization with wire-length prediction
(WP-Fanout)
  • Net selection
  • Select all large-degree nets
  • Select small-degree nets with large net range
  • Net decomposition

LT-tree
Balanced tree
Circuit depth
19
Experiment setting
  • LGSyn93 benchmark suite
  • Optimized by script.rugged
  • Mapped with 0.13um industrial standard cell
    library
  • Placement is done by mPL4
  • Global routing is done by Labyrinth

20
Experimental results
  • Compare with the traditional area-driven
    technology mapping algorithm implemented in SIS
  • Results of the WP-Map algorithm
  • Results of combined WP-Map and WP-Fanout algorithm

21
Compare WP-Map with SIS
Compare mapped netlists
22
Compare WP-Map with SIS (cont.)
Average cut number distribution of C6288
23
Compare WP-Map with SIS (cont.)
Results after placement and global routing
24
Compare WP-Map WP-Fanout with SIS
Results after placement and global routing
25
Conclusions
  • Wire length can be predicted in structural level
  • Mutual contraction
  • Net range
  • Wire length prediction technique can be applied
    into technology mapping and fanout optimization
  • 8.7 improvement on average congestion
  • 17.2 improvement on peak congestion

26
Future work
  • Logic extraction with wire-length and congestion
    prediction
  • Timing-driven technology mapping with wire-length
    prediction
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