Integrating AI And Data Engineering For Intelligent Semiconductor Chip Design And Optimization

Abstract

Artificial intelligence (AI) and machine learning (ML) are revolutionizing industries globally and are rapidly emerging in the electronic design automation (EDA) industry, especially in VLSI design and technology. Recently, they have gained the attention of not only academia but also industries since AI/ML chiplets can meet the urgent demands in generative AI workloads with a huge amount of data involved [1]. AI/ML algorithms are being increasingly explored and applied at multiple levels in the VLSI design and technology flow, yielding various major solutions in the form of new models, methods, and tools to address different bottlenecks, challenges, and opportunities. However, there exist ample opportunities to further exploit AI/ML algorithms in the semiconductor chip design and optimization industry .

This special issue aims to present high-quality research articles and comprehensive review papers covering recent methods, models, algorithms, tools, results, and various exploratory or expository studies of state-of-the-art AI/ML algorithms and applications in VLSI design and technology. Potential topics and contributions include AI/ML applications at various abstraction levels in the VLSI design chain, benchmark suit chronicles and challenges for AI/ML algorithms in VLSI design, AI/ML hardware accelerator architecture, models, designs, and tools, AI/ML privacy protection and explainability in VLSI design and technology, case studies involving AI/ML [1]algorithms in real-life chip designs or manufacturability, surrogate models and physical insights generating models using AI/ML methods, standard datasets and data processing/exploration searching for wide applications of AI/ML in VLSI design, systematic and novel approaches integrating AI/ML algorithms into traditional analysis/optimization flows or algorithms, and successful AI/ML algorithms inspiring VLSI design engineers or researchers to continue a career in the emerging area.