Machine Learning in Advanced IC Design: A Methodological Survey

Chen, Tinghuan; Zhang, Grace Li; Yu, Bei; Li, Bing; Schlichtmann, Ulf

doi:10.1109/mdat.2022.3216799

Cited by 11 publications

(1 citation statement)

References 61 publications

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“…Recently, machine learning (ML) methods have been developed and achieved significant success in the field of computer-aided design (CAD) [25], [26]. ML transforms traditional analysis, modeling, and optimization problems into data-to-data mapping problems, providing efficient and accurate performance evaluation at various design stages [27]- [30].…”

Section: B Model-based Eda Approachesmentioning

confidence: 99%

Unet-Astar: A Deep Learning-Based Fast Routing Algorithm for Unified PCB Routing

Yin,

Jin,

Chen

et al. 2023

IEEE Access

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In recent years, there has been extensive research on the routing problem of printed circuit boards (PCBs). Due to the increasing number of pins, high pin density, and unique physical constraints, manual PCB routing has become a time-consuming task to achieve design convergence. Previous work decomposed the problem into escape routing and area routing, focusing on these problems separately. However, there was always a gap between these two problems, requiring significant human effort for iterative algorithm adjustments. Furthermore, previous area routing work mainly focused on routing between ball grid array (BGA) packages in escape routing. However, in practice, many components are not in the form of BGA packages, such as passive devices, decoupling capacitors, and through-hole pin arrays. Therefore, it is necessary to study a unified routing approach. The current unified routing approach adopts the A* algorithm, but there is still room for improvement in routing speed. This paper proposes a new algorithm called Unet-Astar, which accelerates the routing efficiency by employing deep learning algorithms in a simulated environment. Additionally, a Deeper Unet is proposed for generating recommended regions for the routing algorithm. The new network structure can provide more contextual information, thereby improving routing efficiency. Experimental results demonstrate the effectiveness and efficiency of the proposed algorithm. Specifically, for all given test cases, our router achieves approximately a 70% improvement in runtime speed compared to the old router. Another major contribution of this work is the development of a routing problem set generator, which can generate parameterized routing problem sets with different sizes and constraints. This enables the evaluation of different routing algorithms and the generation of training datasets for future data-driven routing methods. All the code has been open-sourced and can be found at https://github.com/Firesuiry/Unet-Astar-For-PCB-Routing. INDEX TERMS physical design, printed circuit board, routing, Machine learning I. INTRODUCTION 1 This article has been accepted for publication in IEEE Access.

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Section: B Model-based Eda Approachesmentioning

confidence: 99%

Unet-Astar: A Deep Learning-Based Fast Routing Algorithm for Unified PCB Routing

Yin,

Jin,

Chen

et al. 2023

IEEE Access

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Applications of AI/ML Algorithms in VLSI Design and Technology

Singh,

Singh

2024

Integrated Devices for Artificial Intelligence and VLSI

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Large circuit models: opportunities and challenges

Chen,

Chu

et al. 2024

Sci. China Inf. Sci.

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Within the electronic design automation (EDA) domain, artificial intelligence (AI)-driven solutions have emerged as formidable tools, yet they typically augment rather than redefine existing methodologies. These solutions often repurpose deep learning models from other domains, such as vision, text, and graph analytics, applying them to circuit design without tailoring to the unique complexities of electronic circuits. Such an “AI4EDA” approach falls short of achieving a holistic design synthesis and understanding, overlooking the intricate interplay of electrical, logical, and physical facets of circuit data. This study argues for a paradigm shift from AI4EDA towards AI-rooted EDA from the ground up, integrating AI at the core of the design process. Pivotal to this vision is the development of a multimodal circuit representation learning technique, poised to provide a comprehensive understanding by harmonizing and extracting insights from varied data sources, such as functional specifications, register-transfer level (RTL) designs, circuit netlists, and physical layouts. We champion the creation of large circuit models (LCMs) that are inherently multimodal, crafted to decode and express the rich semantics and structures of circuit data, thus fostering more resilient, efficient, and inventive design methodologies. Embracing this AI-rooted philosophy, we foresee a trajectory that transcends the current innovation plateau in EDA, igniting a profound “shift-left” in electronic design methodology. The envisioned advancements herald not just an evolution of existing EDA tools but a revolution, giving rise to novel instruments of design-tools that promise to radically enhance design productivity and inaugurate a new epoch where the optimization of circuit performance, power, and area (PPA) is achieved not incrementally, but through leaps that redefine the benchmarks of electronic systems’ capabilities.

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Machine Learning in Advanced IC Design: A Methodological Survey

Cited by 11 publications

References 61 publications

Unet-Astar: A Deep Learning-Based Fast Routing Algorithm for Unified PCB Routing

Unet-Astar: A Deep Learning-Based Fast Routing Algorithm for Unified PCB Routing

Applications of AI/ML Algorithms in VLSI Design and Technology

Large circuit models: opportunities and challenges

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