arXiv subjects

Mihai Dragos Rotaru

Publications and source records attributed to Mihai Dragos Rotaru.

2 recordsLinked to original sources

arXiv2026

FAPlace: Joint Optimization of Chiplet Placement and Interposer Footprint for 2.5D Systems

The placement of chiplets on a silicon interposer is a pivotal step in 2.5D system integration, yet existing placement approaches typically assume a pre-defined interposer footprint. This creates a circular dependency: the optimal footprint cannot be known without first solving the placement, while the placement itself is constrained by the given dimensions. An undersized interposer may exclude feasible placements, while an oversized one yields unnecessarily sparse solutions. Moreover, even when the footprint area is minimized, few existing approaches explicitly control the interposer's aspect ratio. To jointly address these challenges, we propose FAPlace, a footprint aware mask guided sequential placement framework. FAPlace operates on a sufficiently large canvas, eliminating the circular dependency by allowing the optimal interposer footprint to emerge as an output of the optimization rather than a pre-specified input. At its core is a novel footprint mask that fuses area compactness with an aspect ratio penalty into a unified spatial cost map. Integrated with wirelength and thermal guidance masks, FAPlace delivers holistic multi-physics optimization in a deterministic, single pass process. Experimental results demonstrate that FAPlace reduces wirelength and footprint area while achieving near-unity aspect ratios, without compromising on thermal performance.

cs.AR↗

arXiv2026

TDPNavigator-Placer: Thermal- and Wirelength-Aware Chiplet Placement in 2.5D Systems Through Multi-Agent Reinforcement Learning

The rapid growth of electronics has accelerated the adoption of 2.5D integrated circuits, where effective automated chiplet placement is essential as systems scale to larger and more heterogeneous chiplet assemblies. Existing placement methods typically focus on minimizing wirelength or transforming multi-objective optimization into a single objective through weighted sum, which limits their ability to handle competing design requirements. Wirelength reduction and thermal management are inherently conflicting objectives, making prior approaches inadequate for practical deployment. To address this challenge, we propose TDPNavigator-Placer, a novel multi-agent reinforcement learning framework that dynamically optimizes placement based on chiplet's thermal design power (TDP). This approach explicitly assigns these inherently conflicting objectives to specialized agents, each operating under distinct reward mechanisms and environmental constraints within a unified placement paradigm. Experimental results demonstrate that TDPNavigator-Placer delivers a significantly improved Pareto front over state-of-the-art methods, enabling more balanced trade-offs between wirelength and thermal performance.

cs.LG↗