1. ECE 556 Project Presentation
SAGA-The Unified Algorithm
Phillip Heyrman, Matt Kumerow, & Todd Tisch

2. 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

3. 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

4. The SAGA Algorithm….

6. 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

7. SAGA’s 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

8. Input File Format
Line 1: <number of cells> <number of wires>
Line 2 to number of cells + 1:
<height> <length> <pins> <x pin location> <y pin location> <connects to>

9. SAGA’s Computations Absolute position of each cell
Orientation and possible reflection(s) of each cell
Rectangle B defining the shape of the chip

10. 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