In heterogeneous integration, different dies may employ distinct technologies, making floorplanning across multiple dies inherently coupled with technology assignment. By assuming a fixed technology, almost all prior floorplanning studies were developed without addressing the challenge of technology assignment. This work presents the first systematic study of multi-die floorplanning that treats technology choice as a variable. To address the challenge of variable block areas, we incorporate a recent machine learning technique for rapid PPA estimation. Our methods jointly optimize area, wirelength, performance, power, and cost, thereby highlighting the importance of technology assignment. Experimental evaluations, validated with a commercial tool for both 2.5D and 3D ICs, demonstrate that our systematic optimizations significantly outperform a greedy approach.
@article{arxiv.2502.10932,
title = {Simultaneous Multi-die Floorplanning and Technology Assignment},
author = {Cristhian Roman-Vicharra and Prianka Sengupta and Runzhi Wang and Yiran Chen and Jiang Hu},
journal= {arXiv preprint arXiv:2502.10932},
year = {2026}
}