From Edge AI to On-Device LLMs: Hardware Architectures, Systems, and Applications
A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".
Deadline for manuscript submissions: 15 September 2026 | Viewed by 774
Special Issue Editor
Interests: AI for science; multi-modal LLM systems
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The deployment of artificial intelligence on edge devices has become a critical research frontier, driven by the demand for real-time processing, enhanced privacy, and reduced latency. From traditional deep learning models powering computer vision and speech recognition to the recent emergence of large language models (LLMs) and generative AI, the ability to run sophisticated AI directly on resource-constrained devices is transforming how intelligent systems are designed and deployed.
On-device AI encompasses a broad spectrum of capabilities. Traditional deep neural networks, including convolutional neural networks (CNNs) and recurrent architectures, continue to serve as the backbone for many edge applications such as object detection, image classification, and sensor data analysis. Meanwhile, the rapid advancement of generative AI and LLMs has created new opportunities and challenges for edge deployment, enabling on-device text generation, multimodal understanding, and intelligent conversational agents without cloud dependency.
Realizing efficient on-device AI requires innovations across the full technology stack. Advances in neural network accelerators, energy-efficient processor architectures, and heterogeneous computing platforms are enabling increasingly powerful models to run on edge hardware. Techniques such as model compression, quantization, neural architecture search, and efficient attention mechanisms are bridging the gap between model capability and device constraints. Furthermore, distributed computation frameworks and edge-cloud collaborative architectures allow complex AI workloads to be partitioned and executed across multiple nodes, balancing performance, latency, and resource utilization.
This Special Issue invites contributions that advance the hardware architectures, system designs, and algorithmic innovations, enabling efficient on-device and distributed AI deployment. We welcome research spanning traditional deep learning systems, emerging generative AI and LLM implementations, distributed computation frameworks, and the hardware–software co-design approaches that make edge intelligence practical. Both theoretical advances and real-world implementations are encouraged.
Dr. Ron (Rongyu) Lin
Guest Editor
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Keywords
- on-device AI
- edge generative AI
- on-device LLM
- neural network accelerator
- model compression and quantization
- distributed computation
- hardware–software co-design
- edge-cloud collaborative architecture
- TinyML
- federated learning
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