Floorplanning is a critical phase in VLSI physical design, focusing on determining block positions while optimizing wirelength under specified area constraints. However, classical analytical-based floorplanners are highly sensitive to the quality of initial solutions and existing learningbased methods often suffer from high computational inefficiency and complexity. In this paper, we propose a customized diffusion model to directly generate high-quality initial floorplans. By leveraging a classical analytical-based floorplanner on top of this initial floorplan, the final floorplanning results are significantly improved. To enhance feature extraction, a heterogeneous graph neural network (HGNN) is developed to explicitly incorporate block-to-block and pin-to-block relationships from the netlist during the diffusion process. Additionally, a novel guidance sampling function is introduced to optimize both wirelength and overlap, effectively reducing the required sampling steps while maintaining competitive initial solutions. Experimental results demonstrate that integrating our proposed diffusion model with an advanced analytical-based floorplanner achieves at least 4.8% reduction in runtime and 3.0% reduction in HPWL compared to the original floorplanner and other diffusion-based methods.

As routability has emerged as a critical task in modern field-programmable gate array (FPGA) physical design, it is desirable to develop an effective congestion prediction model during the placement stage. Given that the interconnection congestion level is a critical metric for measuring the routability of FPGA placement, we utilize that level as the model training label. In this paper, we propose a multiscale feature attention (MFA) and transformer based congestion prediction model to extract placement features and strengthen their association with congested areas for effective FPGA macro placement. A convolutional neural network (CNN) component is first designed to extract multiscale features from grid-based placement. Then, a well-designed MFA block is proposed that utilizes the dual attention mechanism on both spatial and channel dimensions to enhance the representation of each multiscale feature. By incorporating MFA blocks and CNN's output at each skip connection layer, our model substantially enhances its capability to learn features and recover more precise congestion level maps. Furthermore, multiple transformer layers that employ dynamic attention mechanisms are utilized to extract global information, which can significantly improve the difference between various congestion levels and enhance the ability to identify these levels. Based on the ten most congested and challenging benchmarks from the MLCAD 2023 FPGA macro placement contest, experimental results show that our model outperforms existing congestion prediction models. Furthermore, our model can achieve the best routability and score among the contest winners when integrated into the macro placer based on DREAMPlaceFPGA.

In very large scale integration (VLSI) circuit physical design, precise congestion prediction during placement is crucial for enhancing routability and accelerating design processes. Existing congestion prediction models often encounter challenges in handling multimodal information and lack effective fusion of placement and netlist features, limiting their prediction accuracy. In this article, we present a novel congestion prediction model that leverages dual multimodal fusions with convolution and transformer layers to effectively capture the multiscale placement information and enhance congestion prediction accuracy. We first adopt convolutional neural networks (CNNs) to extract grid-based placement features and heterogeneous graph convolutional networks (HGCNs) to extract netlist information. To help the model understand the correlation between different modalities, we then propose an early feature fusion (EFF) to integrate netlist knowledge into multiscale placement features at multimodal interaction subspace. Besides, a deep feature fusion (DFF) method is proposed to further fuse multimodal features, which has multiple vision transformer layers based on adaptive attention enhancement technology. These layers include self-attention (SA) to boost intramodal features and cross-attention (CA) to perform cross-modal feature fusion on netlist and grid-based placement features. Finally, the output features of DFF are sent into the cascaded decoder to recover the congestion map by exploiting several upsampling layers and merging with EFF features. Compared with the existing state-of-the-art congestion prediction models, experimental results demonstrate that our model not only outperforms them in prediction accuracy, but also excels in reducing routing congestion when integrated into the placer DREAMPlace.