As modern VLSI design advances, the significance of multi-FPGA systems in prototyping and verification is steadily growing. Due to the physical I/O limitations, the Time-Division Multiplexing (TDM) and I/O assignment techniques are introduced to solve these problems. However, most multi-FPGA systems primarily focus on inter-FPGA routing while overlooking intra-FPGA routing. In this article, a comprehensive routing framework based on TDM for Multi-FPGA systems (RTMF) is hereby presented. To our knowledge, this is the first attempt to jointly optimize intra-level and inter-level routing in the work of multi-FPGA systems (MFS). The RTMF framework, tailored for system-level and intra-level routing under constrained wiring resources, integrates routing demands within and between FPGAs. Through the integration of TDM technology and adaptive optimization algorithms, RTMF effectively meets routing requirements and delivers efficient solutions. Furthermore, RTMF demonstrates remarkable adaptability, allowing for dynamic adjustments and optimizations to address diverse routing demands and constraints. In comparison to the state-of-the-art methodologies, in benchmark designs with a scale greater than 50,000, our approach on average reduces the maximum routing weight by 59.98% and 46.70%, respectively.

Predicting hotspot locations in the early stage of Design Rule Check (DRC) is crucial for designers to proactively prevent design rule violations. However, obtaining an accurate and efficient predictor faces significant challenges due to the influence of available information and severe data imbalance. In this study, we investigate the potential of utilizing Graph Neural networks (GNN) to address this challenge. Our focus is specifically on accurately predicting DRC hotspot locations without relying on global routing techniques. We consider the presence of macros in mixed-size designs. We propose an adaptive adjacency matrix that demonstrates superior application effectiveness compared with traditional adjacency matrices. Furthermore, experimental results on benchmark circuits show significant improvements in the true positive rate (22.38% for the RouteNet model and 26.90% for the GNN model) and accuracy (6.97% and 6.76%, respectively) compared with these models. Our proposed model also maintains a low false positive rate and outperforms other Convolutional Neural Network and GNN models. Additionally, its efficient learning capability and lower computational time contribute to its outstanding training performance, with training time being approximately 10% of that required by other models.

In VLSI design, the utilization of Design Rule Check (DRC) tools in the early stage is crucial for predicting and resolving violations, thereby expediting the physical design process. In our study, we present an efficient model that predicts DRC violations prior to the routing stage. Additionally, our model incorporates a sliding-window technique to enhance the feature extraction process. We extract structural features using Graph Convolutional Networks and utilize feature reuse techniques to fully recover the lost information in neural layers, which serves as input to the Convolutional Neural Network model, resulting in more accurate hotspot prediction. The experimental results demonstrate that our model successfully identifies 95.78% of DRC violations, with a mere 4.17% false-alarm rate. Not only does our method deliver improved feature preprocessing results, but it also enhances prediction accuracy compared to alternative approaches.

Multi-field programming gate array (FPGA) systems are widely used in various circuit design-related areas, such as hardware emulation, virtual prototypes, and chiplet design methodologies. However, a physical resource clash between inter-FPGA signals and I/O pins can create a bottleneck in a multi-FPGA system. Specifically, inter-FPGA signals often outnumber I/O pins in a multi-FPGA system. To solve this problem, time-division multiplexing (TDM) is introduced. However, undue time delay caused by TDM may impair the performance of a multi-FPGA system. Therefore, a more efficient TDM solution is needed. In this work, we propose a new routing sequence strategy to improve the efficiency of TDM. Our strategy consists of two parts: a weighted routing algorithm and TDM assignment optimization. The algorithm takes into account the weight of the net to generate a high-quality routing topology. Then, a net-based TDM assignment is performed to obtain a lower TDM ratio for the multi-FPGA system. Experiments on the public dataset of CAD Contest 2019 at ICCAD showed that our routing sequence strategy achieved good results. Especially in those testcases of unbalanced designs, the performance of multi-FPGA systems was improved up to 2.63. Moreover, we outperformed the top two contest finalists as to TDM results in most of the testcases.