Fuzzy Logic Placement

Emily BlemECE556 Final ProjectDecember 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

Not well addressed by most algorithms

Methods(1) Fuzzy set: a group of objects with different levels of

membership Objects may partially belong to a set Operations: 1 and 2 = max(1, 2)

1 or 2 = min(1, 2)

(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 added

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