# Fuzzy Logic Placement

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Fuzzy Logic Placement

Emily Blem
ECE556 Final Project
December 14, 2004

Reference: E. Kang, R.B. Lin, and E. Shragowitz. “Fuzzy Logic Approach to VLSI
Placement.” IEEE Trans. On VLSI Systems. V. 2, No. 4. December 1994.
Objectives

   Multiple placement
objectives: timing, chip
size, interconnection
length, etc.
   Need a framework in
which to resolve
multiple objectives
most algorithms
Methods(1)
   Fuzzy set: a group of objects with different levels of
membership
   Objects may partially belong to a set
   Operations:     m1 and m2 = max(m1, m2)
m1 or m2 = min(m1, m2)

(image from Kang et. al. 1994)
Methods (2)
   Can be applied to iterative or constructive design
   In an iterative design, reduce number of criteria for
each objective to 1 or 2 due to time constraints
   Constructive algorithm:
   Top level: place cells in feasible regions based on timing
requirements
   Middle level: assign cells to feasible intervals
   Bottom level: assign each cell to a position within its
feasible interval
   Assignment completed row by row based on fuzzy logic
decision maker (FZDM)
Methods(3)
   Small chip area rules:
   If a candidate cell provides good utilization of existing feed
through pins and a small # of rows is used for each net
connected to it, then a small # of feed through cells will be
   If a candidate cell adds a small # of feed through cells and
produces almost equal row length, then small chip area will
be generated
   For large designs, criterion 1/2/1 has a not so strong
preference over criterion 1/2/2
   In early stages of placement, criterion 1/2/1/1 has a strong
preference over criterion 1/2/1/2
   In middle stages of placement, criterion 1/2/1/2 has a mild
preference over criterion 1/2/1/1
Results
   Ability to tune solution a key feature of fuzzy
placement
   In paper, (balanced) fuzzy placement consistently
outperformed TimberWolf6.1 and OASIS using
same routers after placement

design name                     Fract             Struct         Biomed
placer                   TW6.1     Fuzzy   TW6.1     Fuzzy   TW6.1   Fuzzy
propagation delay (ns)   1.12      0.65    6.15      5.32    13.32   10.9
chip area (mm2)          0.53      0.53    6.89      6.80    51.34   51.40

(data from Kang et. al. 1994)
Conclusions
   FZDM avoids issues of greedy placer in constructive
placement
   In iterative placement, CPU time issues make FZDM
into a weighted cost function
   According to paper, achieves impressive results
   Fuzzy logic structure makes it easy to tune solution
for different goals and achieve multiple objectives

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