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Neuromorphic computing is rapidly emerging as a transformative paradigm for intelligent systems, leveraging biologically inspired principles to overcome traditional performance and scalability bottlenecks. Recent breakthroughs in hardware, including mixed-signal neuromorphic processors, photonic computing elements, and non-volatile memory devices such as spintronics and memristive arrays, are enabling ultra-low-power, event-driven architectures tailored for real-time edge intelligence and post-Moore innovation.
Simultaneously, advances in computational models, including spiking neural networks, synaptic plasticity mechanisms, and hybrid analog-digital frameworks, are redefining how learning and adaptation can be embedded natively in hardware. The tight co-design between physical substrates and algorithmic behavior offers new frontiers for energy efficiency, robustness, and contextual learning across domains ranging from autonomous sensing to cognitive robotics.
This special issue of IEEE Computer Magazine will illuminate the convergence of architectural innovation and adaptive algorithms within neuromorphic systems. It aims to capture multidisciplinary progress in design methodologies, hardware-software integration, fabrication technologies, and real-world deployments. Contributions will showcase both foundational research and scalable implementations, fostering deep collaboration across academia, industry, and government labs. The issue will be of particular interest to researchers and practitioners in AI hardware acceleration, embedded and cyber-physical systems, post-Von Neumann architectures, and cognitive computing.
The topics of interest include, but are not limited to:
For author information and guidelines on submission criteria, visit the Author’s Information Page. Please submit papers through the IEEE Author Portal and be sure to select the special issue or special section name. Manuscripts should not be published or currently submitted for publication elsewhere. Please submit only full papers intended for review, not abstracts.
In addition to submitting your paper to Computer, you are also encouraged to upload the data related to your paper to IEEE DataPort. IEEE DataPort is IEEE's data platform that supports the storage and publishing of datasets while also providing access to thousands of research datasets. Uploading your dataset to IEEE DataPort will strengthen your paper and will support research reproducibility. Your paper and the dataset can be linked, providing a good opportunity for you to increase the number of citations you receive. Data can be uploaded to IEEE DataPort prior to submitting your paper or concurrent with the paper submission. Thank you!
