Floorplanning is the process of placing a set of rectangular blocks on a chip. Huge blocks, like a RAM or a whole circuit module. As such, it is a very specific domain in placement: floorplanning usually handles less than 100 cells (to be compared with the ~100000 handled by other methods) but with entirely different algorithms.
Since the laboratory where I work has an important analog toolchain – for the design of circuits that are not purely digital, typically for wireless transmissions or sensors – I gave a try to algorithms to place the resulting transistors.
The constraints for analog circuit placement
I am not an analog designer myself, so I tried to gather informations about the needs in the analog world. The recurring answer was “they want to chose” between different possibilities, because the designers’ knowledge is not easily mimicked by an algorithm yet.
Providing this freedom is probably the hard part, but there were more satisfying answers for me too, that could lead directly to a toy implementation. Symmetry constraints, in order to mitigate process variations. Proximity constraints. Area minimization. Routing corridors. Those are easier to translate into an algorithm.
Moreover, analog circuits have some freedom during placement: the transistors are big, and it is possible to change their aspect ratio.
Integer programming for floorplanning
Most tools for floorplanning work on some kind of topological representation: block a is above block b, which is on the left of block c. This is usually limited to area optimization only, but linear programming can handle more complex situations.
In those situations, including deformable blocks and wirelength minimization, linear programming can yield a solution for the given topology. Rather than writing complex data structures to represent the topology, I included it in an integer programming model: it is simpler, requires less code, and ideally it would even prove the solution’s optimality.
With the tool I use (GLPK), I obtain what I consider to be good results. On a small real benchmark with 7 cells, it proves optimality relatively quickly. It is generally not able to prove optimality as soon as there are more than 10 cells, although local search heuristics are extremely efficient.
It remains to be seen whether such a floorplanner would be useful to an analog designer. It seems to me that obtaining a good enough placement quickly is important, but they would surely want to fine-tune it. For this trial-and-error process, tuning the model may be the way to go, giving block deformation and shifting for free whenever a cost or a constraint is modified.
This work gave me some insight in linear programming for digital detailed placement: I am experimenting with similar models to place standard cells in the main tool.