Matheuristic Local Search for the Placement of Analog Integrated Circuits

TL;DR

This paper introduces a matheuristic local search method to enhance ILP-based placement of analog integrated circuits, significantly improving solution quality for large, complex instances with limited computation time.

Contribution

It proposes a novel local search technique combined with ILP constraints to better optimize analog IC placement, addressing limitations of existing methods.

Findings

Significant improvement in placement quality for large instances.

Effective local search within limited computation time.

Validated on synthetic and real-world IC placement problems.

Abstract

The suboptimal physical design of the integrated circuits may not only increase the manufacturing costs due to the larger size of the chip but can also impact its performance by placing interconnected rectangular devices too far from each other. In the domain of Analog and Mixed-Signal Integrated Circuits (AMS ICs), placement automation is lacking behind its digital counterpart, mainly due to the variety of components and complex constraints the placement needs to satisfy. Integer Linear Programming (ILP) is a suitable approach to modeling the placement problem for AMS ICs. However, not even state-of-the-art solvers can create high-quality placements for large problem instances. In this paper, we study how to improve the results of our previous ILP model, first by introducing additional constraints and second by using matheuristics. Given the initial solution we obtain using our original ILP model, we use the solver to perform a local search. We try to improve the criterion by considering only a few spatially close rectangles while keeping the rest of the placement fixed. This local search approach enables us to significantly improve the quality of instances whose solution space we could not sufficiently explore before, even when the computation time reserved for the matheuristic is limited. Finally, we evaluate our revised approach on synthetically generated instances containing more than 200 independent rectangles and on real-life problems.