Design Automation for Embedded Systems

Aims and 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, synthesis, and optimization for embedded systems, FPGAs, 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. 

Special Issue on AI+ML for Systems Design
Día de Entrega: 2026-06-30

The use of AI and machine learning is transforming how complex hardware–software systems are designed and optimized. From novel architectures to automated design flows and improved algorithms, these techniques are opening up exciting new avenues for research in system design and optimization.

This special issue of the Journal of Design Automation for Embedded Systems invites papers on all aspects of using AI/ML for systems design, including but not limited to the following areas:

• 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 systems design research.
• Data science and analytics techniques applied to design automation pipelines, performance tuning, and debugging.
• Real-world case studies of applying AI techniques to existing systems.
• New commercial EDA design tools and flows leveraging AI/ML techniques.

All submitted manuscripts will be subject to the standard review process of the Journal. Prospective authors should submit their manuscripts using Springer Nature submission system under collection type “Special Issue on AI+ML for Systems Design”.

- Submission Deadline: March 1, 2026
- Reviews and Revisions Cycle: March/April, 2026
- Notification of Acceptance: May 4, 2026
- Target Publication Date: June 2026

https://link.springer.com/collections/fhjbbgjbjd