Scilight Press

Edge Intelligence and Systems (EIS) is a peer-reviewed, open-access international journal dedicated to disseminating pioneering research and innovative practices at the intersection of artificial intelligence (AI), edge computing, and their integration into system design. The journal champions research that unleashes the full potential of modern and sophisticated AI in pervasive edge scenarios, driven by transformative research on algorithms, software, hardware, networking, architectures, systems, and applications that define the future of edge-native intelligent, interactive, and autonomous technologies. EIS is published quarterly online by Scilight Press.

The scope of EIS encompasses the entire technology stack for edge intelligence and systems, from foundational intelligence and computing paradigms to high-level multidisciplinary applications. We welcome original research papers, review articles, and short communications on the following (but not limited to) topics:

Edge-Native AI

This area focuses on the development of AI models and algorithms designed for edge environments, including large language models and generative AI that enable efficient on-device inference, embodied and agentic intelligence, collaborative and decentralized learning, as well as efficient adaptation techniques such as model compression, quantization, neural architecture search, and continual learning. We welcome contributions that propose novel methods, architectures, or frameworks that advance the understanding and capabilities of edge-native AI.

Systems and Hardware for Edge AI

This area addresses system architectures, hardware acceleration, and software–hardware co-design for distributed AI across devices, edge nodes, and cloud infrastructure. We welcome research on resource orchestration, neuromorphic and domain-specific accelerators, and strategies that enhance the performance, efficiency, and scalability of edge AI deployments.

Networking for Distributed Intelligence

This area covers communication and networking technologies that support distributed AI and edge intelligence, encompassing low-latency protocols, intelligent routing, and AI-driven network management. We welcome studies that advance the design and optimization of networking solutions for real-time, reliable, and scalable distributed intelligence.

Embodied AI and Advanced Use Cases

This area highlights embodied edge intelligence and advanced applications in complex real-world scenarios, enabling on-device intelligent systems to operate autonomously, collaboratively, and responsively. It includes embodied and agentic learning, interactive and collaborative robotics, autonomous systems, on-device intelligent assistants, and human–AI interaction, as well as industrial digital twins, cyber–physical systems, and industrial metaverse applications that enable seamless synchronization between physical and virtual environments.

Trustworthy and Responsible Edge AI

This area emphasizes the development of edge AI systems that are secure, ethical, and reliable in complex real-world environments. It covers advanced cryptographic and privacy-preserving techniques for decentralized models and data, frameworks ensuring explainability, fairness, and accountability for real-world autonomous agents, and approaches to guarantee robustness and predictability in mission-critical applications.