Neuromorphic computing has the potential to transform the way we use computers through new materials, new brain-inspired chips, greater understanding of neuroscience, and breakthroughs in low-energy/edge AI. Neuromorphic computing systems, we believe, will lead to more powerful and efficient computing paradigms. The goal of this conference is to bring together leading researchers in neuromorphic computing to present new research, develop new collaborations, and provide a forum to publish work in this area.

Focus Areas

We welcome submissions in the areas of neuromorphic computing, neural-inspired computing, and neuroscience-inspired AI. Below is a list of focus areas of particular interest.

Systems, architectures, circuits, devices, and materials
Neuromorphic circuits or sensors
Non-von Neumann computing architectures and models
Emerging devices and hardware implementations
Event or spike-based systems
Novel brain-inspired system architectures
Algorithms for training, configuring, or programming
Supervised, unsupervised and self-supervised learning methods
Biologically-inspired algorithms
Adaptations to existing algorithms for use on or with neuromorphic systems
Continual learning systems
Applications and use-cases
Energy Efficient Edge-AI Applications
Biomedical Applications
Applications demonstrating a neuromorphic or neural-inspired advantage
Benchmark tasks for neuromorphic computing
Neuromorphic datasets
Domain-specific adaptation
Software and interfaces for neuromorphic systems
Efficient simulation techniques for large-scale networks
Compilers, programming frameworks and related methodology
Visualization and insight tools

Note: This is a non-exhaustive list and submissions outside the scope of these areas will also be considered.