Design Automation for Embedded Systems

Aims & Scope

Design Automation for Embedded Systems is a multidisciplinary journal dedicated to cutting-edge research in the automation of embedded systems design. As embedded systems evolve to meet the demands of increasingly complex and intelligent applications—spanning sectors from automotive and aerospace to IoT and edge AI—the need for advanced design methodologies and tools has never been greater. The journal focuses on innovations that push the boundaries of how embedded systems are architected, designed, verified, and deployed. Core areas of interest include: Hardware-Software Co-design, s ynthesis, and optimization for embedded systems, FPGA s , multicore, and heterogeneous systems AI-assisted design and optimization workflows , including agentic systems and reinforcement-learning-based automation System-level methodologies leveraging large language models (LLMs) for specification, code synthesis, hardware/software partitioning, and design-space exploration LLM-aided tools for Register-Transfer Level (RTL) design, High-Level Synthesis (HLS), and formal verification AI hardware acceleration and co-design methodologies for deploying machine learning workloads on edge and embedded platforms New datasets, benchmarks, and evaluation frameworks that enable reproducibility and standardization in embedded system design research Data science and analytics techniques applied to design automation pipelines, performance tuning, and debugging We welcome contributions that blend embedded systems with advances in artificial intelligence, computer architecture, design methodology , and software engineering. High-quality submissions may include theoretical innovations, practical tools and frameworks, novel architectures , empirical studies, and real-world case studies. Design Automation for Embedded Systems serves as a forum for researchers and practitioners to present transformative ideas that advance the future of intelligent, automated embedded system design.

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Source: OpenAlex · Each year’s green bar = citations earned by that year’s papers, counted to date — so recent years look lower simply because their papers haven’t had time to be cited yet.

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