Neural Networks: From Software to Hardware
A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".
Deadline for manuscript submissions: 15 December 2025 | Viewed by 9
Special Issue Editors
Interests: physical neural networks; battery-free Internet of Things; IoT-augmented metaverse; mobile computing; wireless communication and sensing
Special Issue Information
Dear Colleagues,
Neural networks have undergone a transformative journey from purely software-based models to tightly integrated hardware–software systems, enabling AI to enhance quality of life, drive industrial transformation, and catalyze breakthroughs in scientific research. This Special Issue, “Neural Networks: From Software to Hardware”, aims to explore the intersection of algorithmic innovation and hardware deployment, bridging theoretical advancements with practical utilization. We invite contributions addressing the co-design of neural network architectures and hardware platforms, including but not limited to the following:
- Novel neural network paradigms for hardware deployment (e.g., spiking neural networks with faster inference and higher energy-efficiency, knowledge distillation techniques capable of learning richer information from complex networks using simpler architectures, sparse models with enhanced robustness and generalization capabilities, and model pruning techniques that enable the more precise identification of redundant components in neural networks)
- Flexible hardware architectures compatible with neural networks (e.g., embedded software–hardware interaction systems with lower latency and higher energy-efficiency).
This Issue welcomes original research, reviews, and case studies that highlight breakthroughs in both foundational methodologies (e.g., spiking neural network, knowledge distillation, and model pruning) and cutting-edge applications (e.g., autonomous systems, wireless communication and sensing, and IoT).
Dr. Lei Yang
Dr. Donghui Dai
Guest Editors
Manuscript Submission Information
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Keywords
- model compression
- lightweight network architectures
- inference acceleration
- edge artificial intelligence
- wireless communication and sensing
- IoT
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