The last post gave a quick introduction to the synthesis flow. Since it is the purpose of the tool, I am going to focus on placement algorithms here. Let’s leave all the other topics for now, even if high-level optimization, boolean optimization and technology mapping are equally interesting.

Placement may optimize the integrated circuit for several metrics: power, timing, area and, more importantly, congestion. That is, it must pass a feasible problem to the subsequent routing stage.

The most basic objective is wirelength: it correlates well with all the other metrics and is the primary one for most benchmarks.

So, how does a placer work? Early placers used meta-heuristics, such as simulated annealing. Even with clustering techniques, it becomes difficult to deal with large circuits. Both the industry and the academic world stopped using this technique during the 80s, and they began using two steps: a simplified global placement problem is solved to obtain approximate positions for the cells. It is followed by a legalization to obtain a correct circuit and a detailed placement phase then corrects local suboptimalities.

The global placement phase may be the most important one since it captures the problem’s complexity, while detailed placement is mostly about refinement. Two classes of algorithms have been developped for global placement: partitioning-based placement and analytical placement.

### Placement through graph partitioning

You can see the netlist to place as an (hyper)graph: the cells are the vertices and the nets are the (hyper)edges. Placing the circuit with minimal wirelength is the problem of cutting the graph with minimal cost: you assign parts of the circuit to placement regions with as few wires between them as possible. By partitioning into gradually smaller regions, you can obtain a circuit with few global wiring. This weighted min-cut problem is complex but can be solved efficiently with heuristics: it is at the heart of partitioning-based placement.

Today, the partitioning-based tools are much more complex to capture timing and congestion: all tools, from Capo from the university of Michigan to Dragon and Fengshui have further heuristics to drive partitioning.

### Continous optimization

On the other hand, you may model the wirelength as a continuous function of the cells’ positions: this is the idea behind analytical placement. With a convex model, you may use efficient optimization algorithms. However, the obvious optimal solution is to cluster all cells together, whereas you want to return an almost overlap-free placement. In order to spread the cells, analytical placement needs a companion algorithm that modifies the cost function or constrains the cells to separate regions. The algorithms alternate between optimization and spreading until a sufficiently refined solution is reached.

The choice of these spreading heuristics varies considerably between placement tools. Early tools constrained the cells to regions: it is still the method used by the commercial tool BonnPlace. More recent academic tools pull the cells toward a legal position, the calculation of which is generally purely heuristic. Kraftwerk simulated repulsion with an electrostatic potential while tools like Fastplace, SimPL or MPl just try to spread the cells with partitioning-like heuristics. Notable exceptions are tools that model the legalization problem as a flow problem, which have more mathematical justifications.

Note that no tool that I know of is available for more than benchmarking: they all provide binary-only versions. This is a sufficient reason to create an open tool, and I hope that Coloquinte will fill this gap.

I will not publish code before I have something clean enough for the global placer, but I hope to open the Git within two (one?) months and to gradually publish the algorithms.

In the next post, I will discuss the choice of a specific analytical placement algorithm.