Sezin Kircali Ata

Yubo Hou, Sezin Kircali Ata, Gen Liang Lim et al.

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.

Sezin Kircali Ata, Yuan Fang, Min Wu et al.

Real-world networks often exist with multiple views, where each view describes one type of interaction among a common set of nodes. For example, on a video-sharing network, while two user nodes are linked if they have common favorite videos in one view, they can also be linked in another view if they share common subscribers. Unlike traditional single-view networks, multiple views maintain different semantics to complement each other. In this paper, we propose MANE, a multi-view network embedding approach to learn low-dimensional representations. Similar to existing studies, MANE hinges on diversity and collaboration - while diversity enables views to maintain their individual semantics, collaboration enables views to work together. However, we also discover a novel form of second-order collaboration that has not been explored previously, and further unify it into our framework to attain superior node representations. Furthermore, as each view often has varying importance w.r.t. different nodes, we propose MANE+, an attention-based extension of MANE to model node-wise view importance. Finally, we conduct comprehensive experiments on three public, real-world multi-view networks, and the results demonstrate that our models consistently outperform state-of-the-art approaches.