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Tier partitioning is one of the most critical stages in monolithic 3-D (M3D) integrated circuits (ICs) implementation flows. It transforms 2-D netlists into 3-D by performing tier assignment for each design instance, which directly impacts the power, performance, and area (PPA) metrics of final 3-D full-chip designs. However, the current state-of-the-art tier partitioning approach named bin-based min-cut algorithm has fundamental flaws that lead to severe drawbacks, such as timing degradation, 3-D routing overhead, and redundant monolithic intertier vias (MIVs) insertion. To overcome these issues, in this article, we propose TP-GNN, an unsupervised graph learning-based tier partitioning framework that utilizes graph neural networks (GNNs) and advanced machine learning (ML) techniques to perform tier partitioning. The proposed framework comprehends design- and technology-related parameters properly so that it is generalizable to various netlists and technologies. In addition, it can be integrated with any style of M3D design flows that require tier assignments of standard cells. In the experiments, we validate the proposed framework on seven industrial designs with two different fashions of M3D implementation flows: 1) partitioning-first (Snap3D) and 2) partitioning-last (Shrunk2D and Compact2D) flows. We demonstrate that our framework, TP-GNN, significantly improves the 3-D quality of results (QoR) across most testing designs in a large margin compared with the bin-based min-cut tier partitioning algorithm. Specifically, in OpenPiton, an RISC-V-based multicore system, we observe 27.4%, 7.7%, and 20.3% improvements in performance, wirelength, and energy-per-cycle, respectively. Finally, we perform a case study by applying the proposed framework to a heterogeneous M3D design flow, Pin3D, on a commercial CPU design and observe that TP-GNN reaches better partitioning solutions than the existing partitioning approaches for heterogeneous 3-D ICs. [ABSTRACT FROM AUTHOR]

As Moore’s law with traditional process node scaling is slowing down, other techniques are required for the advancement of process nodes. In this work, we focus on one such alternative: 3-D physical design of integrated circuits (ICs). While many recent studies have shown the benefits of 3-D IC design on timing and power consumption of circuits, routing in 3-D is solely done with the automatic commercial routers and has not been well studied. In this article, we discuss the various routing scenarios that arise from cell partitioning and the metal layer stack in 3-D. Unlike a 2-D IC, the metal layer configuration in 3-D depends on the orientation in which the dies are bonded together. Due to this, depending on the configuration, cells in one tier tend to use routing layers from the other tier. This is referred to as metal layer (or) routing sharing. This depends on the metal layer stack and the cell partitioning in 3-D, as well as the via pitch used for 3-D connections. By analyzing metal layer sharing in detail, we see that it can help reduce metal layer costs in 3-D as well as improve the power consumption and, in some cases, the maximum achievable performance of the circuits. Overall, the 3-D metal layer cost can decrease by 9% along with an improved power delay product of up to 7.5% just from the routing sharing in monolithic 3-D ICs. [ABSTRACT FROM AUTHOR]

In recent years, the size of transistors has been scaled down to a few nanometers and further shrinking will eventually reach the atomic scale. Monolithic three-dimensional (M3D) ICs use the third dimension for placement and routing, which helps reduce footprint and improve power and performance of circuits without relying on technology shrinking. This article explores the benefits of M3D ICs using OpenPiton, a scalable open-source Reduced Instruction Set Computer (RISC)-V-based multicore SoC. With a logic-on-memory 3-D integration scheme, we analyze the power and performance benefits of two OpenPiton single-tile systems with smaller and larger memory architectures. The logic-on-memory M3D design shows 36.8% performance improvement compared to the corresponding tile design in 2-D. In addition, at isoperformance, M3D shows 13.5% total power saving. [ABSTRACT FROM AUTHOR]