Fast System Technology Co-Optimization Framework for Emerging Technologies Using Graph Neural Networks

핵심 개념

A fast STCO framework that leverages graph neural networks to accelerate TCAD simulations, compact model development, and cell library characterization, enabling comprehensive STCO iterations with significant runtime speedups for emerging and traditional semiconductor technologies.

초록

The paper proposes a fast system technology co-optimization (STCO) framework that optimizes power, performance, and area (PPA) for next-generation IC design, addressing the challenges and opportunities presented by novel materials and device architectures. The key contributions are:

TCAD Surrogate Model: The framework employs a graph neural network (GNN)-based approach for TCAD simulations, using a unified device encoding scheme to capture material-level, device-level, and spatial relationship information. This GNN-based surrogate model achieves high accuracy and significantly reduces TCAD simulation time.

Unified Compact Model: The paper develops a compact model that accounts for mobility variations in emerging transistor technologies, such as carbon nanotube (CNT), indium gallium zinc oxide (IGZO), and low-temperature polycrystalline silicon (LTPS), due to charge drift in the presence of tail-distributed traps and variable range hopping. This unified compact model is validated against measured I-V curves from these technologies.

Cell Library Characterization: The framework utilizes a two-stage process to accelerate cell library characterization. First, it generates extensive cell datasets through transistor-level SPICE simulations. Then, it employs a graph convolutional network (GCN) to establish a fast cell characterization model, focusing on the variation of critical parameters like supply voltage, threshold voltage, and gate unit capacitance.

The effectiveness of the proposed framework is validated by evaluating multiple designs using CNT technology, demonstrating runtime speedups ranging from 1.9X to 14.1X over traditional STCO flows. The framework's adaptability allows easy application to other emerging technologies like IGZO and LTPS.

통계

The paper reports the following key metrics:

TCAD simulation time reduced to 1.38 seconds, compared to an average of 142.07 seconds for commercial EDA tools.
Cell library characterization time reduced to 8.88 seconds, compared to nearly 1900 seconds for commercial tools.
Overall STCO flow acceleration ranging from 1.9X to 14.1X over traditional flows per iteration.

인용구

"Our framework significantly enhances computational efficiency, reducing TCAD simulation time to 1.38 seconds and cell library characterization time to 8.88 seconds, beyond a shared environment setup time of 8.12 seconds for both processes."
"In contrast, commercial EDA tools require an average of 142.07 seconds for device simulations, as determined from a calibrated study involving 576 planar CNT devices with 2D TCAD simulations [5], and nearly 1900 seconds for cell library characterizations."

핵심 통찰 요약

Fast System Technology Co-Optimization Framework for Emerging Technology Based on Graph Neural Networks

by Tianliang Ma... 게시일 arxiv.org 04-11-2024

https://arxiv.org/pdf/2404.06939.pdf

더 깊은 질문

How can the proposed framework be extended to incorporate other system-level evaluation tools and methodologies to achieve even greater overall STCO acceleration?

The proposed framework can be extended by integrating additional system-level evaluation tools and methodologies that leverage AI-driven techniques. One approach could be to incorporate machine learning algorithms for logic synthesis, placement & routing, and DRC & LVS checks, which are crucial steps in the design flow. By developing AI models that optimize these processes, the overall STCO acceleration can be further enhanced. Additionally, exploring reinforcement learning agents for system-level optimization tasks could streamline the design process by efficiently exploring the design space and identifying optimal solutions. By combining various AI techniques tailored to specific system evaluation tasks, the framework can achieve even greater acceleration in STCO.

What are the potential challenges and limitations in applying the GNN-based techniques to other emerging semiconductor technologies beyond CNT, IGZO, and LTPS?

While GNN-based techniques have shown promising results in optimizing STCO for CNT, IGZO, and LTPS technologies, there are challenges and limitations in applying these techniques to other emerging semiconductor technologies. One major challenge is the need for extensive data for training GNN models, which may not be readily available for newer technologies. Gathering and labeling large datasets for diverse emerging technologies can be time-consuming and resource-intensive. Additionally, the complexity of the material properties and device architectures in new technologies may require significant model adjustments and hyperparameter tuning to ensure the GNN models' effectiveness. Moreover, the generalizability of GNN models across different semiconductor technologies may be limited, as each technology has unique characteristics that may not be captured effectively by a single model. Adapting GNN-based techniques to new technologies requires careful consideration of these challenges to ensure accurate and reliable optimization results.

How can the cell library characterization model be further improved to enhance the accuracy of predicting dynamic power consumption metrics, such as flip power and non-flip power?

To enhance the accuracy of predicting dynamic power consumption metrics like flip power and non-flip power in the cell library characterization model, several improvements can be implemented. Firstly, increasing the complexity of the model architecture by incorporating deeper graph convolutional networks (GCNs) and additional layers of multilayer perceptrons (MLPs) can capture more intricate relationships within the data and improve prediction accuracy. Fine-tuning the hyperparameters of the model and optimizing the training process can also enhance the model's performance. Furthermore, augmenting the training dataset with more diverse and representative samples can help the model learn a wider range of patterns and variations in dynamic power consumption. Additionally, exploring advanced regularization techniques and data augmentation methods can prevent overfitting and improve the model's generalization capabilities. By iteratively refining the model architecture, optimizing training strategies, and enriching the dataset, the cell library characterization model can be further improved to accurately predict dynamic power consumption metrics for various semiconductor technologies.