ECE 556 Project Presentation

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ECE 556 Project Presentation

• SAGA-The Unified Algorithm

Phillip Heyrman, Matt Kumerow, Todd Tisch
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What is SAGA?

• Combination of Simulated Annealing (SA) and
  Genetic Algorithm (GA)
• Begins as GA and gradually becomes SA
• The speed of the conversion is adaptive because
  it is determined by progress
• Parameters can be setup to run the algorithm in
  pure SA or GA mode

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Why SAGA?

• Has a large number of modifiable parameters that
  can be tuned for program performance
• Past test results have shown that SAGA performs
  equal to or better than most genetic algorithms
• Application independent

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The SAGA Algorithm.
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SAGA Objectives

• No pairs of cells overlap each other
• The rectangle B encloses all cells and has
  approximate horizontal length W
• The area within B which is not occupied by cells
  in sufficiently large to contain all routing
  needed to implement the required interconnections

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SAGAs expected inputs

• Set of rectangular cells each with a number of
  terminals at fixed positions along the edges of
  the cell
• Netlist specifying the interconnections of all
  terminals
• Approximate horizontal length W of the chip

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Input File Format
Line 1 ltnumber of cellsgt ltnumber of
wiresgt Line 2 to number of cells 1 ltheightgt
ltlengthgt ltpinsgt ltx pin locationgt lty pin locationgt
ltconnects togt
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SAGAs Computations

• Absolute position of each cell
• Orientation and possible reflection(s) of each
  cell
• Rectangle B defining the shape of the chip

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Output Data

• Matlab file for graphing of final cell placement
• Output file that contains total wire length,
  final position and orientation of cells, total
  CPU time, final bounding box