Brain Inspired Computing
- Submission status
- Open
- Submission deadline
The semiconductor industry has historically thrived by building larger numbers of smaller transistors on integrated chips (known as Moore’s law) to increase their information and functional density. However, transistor scaling becomes more challenging as the device dimension reaches the nanometer scale, while proportional improvements in computing speed and capacity started saturating around 2006. A major contributor to this issue is the von Neumann bottleneck, i.e., that separation of storage and processing units, which leads to an overall system’s performance constrained by the data movement. On the other hand, rapid advances in data-intensive applications, such as artificial intelligence (AI), have required even faster improvements in chip performance and energy efficiency, resulting in significant mismatches between hardware capabilities and application demands.
Among the various proposals to address the von-Neumann bottleneck, brain-inspired (or brain-like/neuromorphic/neural) computing is one of the most promising and mature avenues. For this Topical Collection, we are inviting Research Articles or Reviews in the following areas:
- Neuromorphic computing algorithms and architectures
- Physical and material neuromorphic implementations
- Hardware implementations of spiking neural networks (e.g. memristor networks)
- Scaling and reliability of brain-inspired architectures and chips
Other topics related to brain-inspired computing are also welcome. We are particularly interested in novel approaches that go beyond the current state-of-the-art.
All submissions will be subjected to the same peer-review process and editorial standards as regular npj Unconventional Computing Articles.
Editors
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Wei Lu, PhD
Professor, Department of Electrical Engineering & Computer Science, University of Michigan, USA
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Yuchao Yang, PhD
Professor, School of Integrated Circuits, Peking University, China Director, Center for Brain Inspired Chips, Institute for Artificial Intelligence, Peking University, China
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Christof Teuscher, PhD
Professor, Department of Electrical and Computer Engineering, Portland State University, USA
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Andy Sarles, PhD
Associate Professor, Department of Mechanical, Aerospace and Biomedical Engineering, University of Tennessee, USA
