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Call for Papers: Next-Generation AI Computing Models and Architectures

Introduction

Artificial intelligence has reached its current state because large foundation models have ushered in a new phase of progress, leading to improved computational capabilities and better system performance. AI workloads today require computing solutions that can scale effectively, use energy resources optimally, and understand hardware requirements, which traditional systems cannot deliver. The existing difficulties have propelled researchers to create advanced AI computing systems that can function across different hardware environments while maintaining system performance, power usage and operational expenses.

Researchers have developed new Next-generation AI computing models through their research on diffusion language models and mixture-of-experts (MoE), linear-time attention mechanisms, and new connectivity paradigms. The combination of new algorithms with existing system limits creates new possibilities for designing advanced artificial intelligence systems and their distribution across multiple systems.

This Special Issue invites original research and high-quality review papers that explore emerging directions in high-performance AI computing and redefine the architectural foundations of intelligent systems.

Scope

  • The Special Issue invites researchers from different fields to submit their work, which will develop new AI theoretical knowledge, architectural systems and technological advancements. Submissions may address, but are not limited to:
  • New architectural designs that enable future AI computing systems
  • Bio-inspired and neuromorphic computing frameworks
  • The combination of neuro-symbolic systems with hybrid artificial intelligence models
  • The development of AI computing systems that operate between cloud and edge computing environments
  • Hardware-efficient AI system co-design strategies
  • The development of AI systems that can operate at a large scale across multiple distributed locations while maintaining environmentally friendly operations
  • The development of new computing systems that will meet the processing needs of advanced artificial intelligence applications
  • The presentation of practical deployment knowledge through industrial case studies and real-world applications.

    • Know More About This Issue

    As AI systems develop greater complexity and larger size, traditional computing systems face difficulties in handling rising performance requirements and energy consumption. The development of high-performance computing needs complete unification between hardware-aware algorithms, compiler/runtime optimizations and distributed execution methods.

    Emerging architectural paradigms are enabling advanced AI system design through tighter integration between model development and hardware capabilities. The future of scalable intelligence will be shaped by innovations that extend from neuromorphic systems to heterogeneous cloud-edge infrastructures.

    The Special Issue enables researchers and practitioners to submit their groundbreaking research that develops AI computing architectures while establishing fundamental knowledge for global intelligent system development.

    AI Computing Models and Architectures

    Key Themes

  • Architectural innovations in AI computing models.
  • AI computing systems use hardware-aware optimisation methods for their architectural design.
  • The system achieves high-performance AI through methods that consume less energy and support sustainable development.
  • The system provides two main capabilities, which include distributed execution that can be scaled and system-level optimisation.
  • Bio-inspired and neuromorphic approaches to intelligent computation
  • Neuro-symbolic integration and hybrid intelligence models
  • Advanced AI systems use cloud-edge collaboration to create their infrastructure.
  • Advanced AI computing models require real-world deployment strategies for their operational implementation.

    • How We Support Your Submission

    The high-impact Special Issue requires researchers to demonstrate their theoretical expertise through precise technical writing abilities. Our expert team supports researchers preparing manuscripts for this Call for papers AI computing initiative through:

  • Academic editing and technical manuscript refinement
  • Guidance on structuring research and review papers
  • Support in presenting architectural and system innovations
  • Language enhancement to meet international publication standards
  • Formatting assistance aligned with journal requirements
  • Expert review support for revision and resubmission.

    • Journal Guidelines:

  • The manuscript requires original research that has not been published or submitted for review at any other publication.
  • The authors need to write all their submissions using high-quality English.
  • The expected length of papers should range between 8 and 10 pages.
  • The authors must adhere strictly to the official “Guidelines for Authors” document.
  • The Editorial Manager (EM) system requires all authors to submit their manuscripts through its platform.
  • The author needs to choose “VSI: Artificial Intelligence” as their Article Type selection during submission.
  • Each paper will be reviewed by at least two independent experts to ensure quality, originality, and novelty.

    • Important Dates

    Submission Deadline: 30 June 2026

    To ensure successful and timely submission to the special issue “Next-Generation AI Computing Models and Architectures,” researchers are encouraged to seek expert publication support from conceptualisation to final submission.

    Free Guide: How to Write the Journal Manuscript

    Book a free consultation to get guidance from the PhD assistance research lab for writing a credible research manuscript and submitting it in the high-quality journal.

    Reference

    Engineering. (2026, February 27). Next-generation AI computing models and architectures: Call for papers. Elsevier. https://www.sciencedirect.com/journal/engineering

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