Neuromorphic Computing and Engineering Community
- Educational content to get you started with the neuromorphic engineering.
- Events about neuromorphic research and software, with contributions from both academia and industry.
- A curated list of neuromorphic open source software and hardware to make it easier to find the tool you need.
- A platform for your code. If you wish to create a new repository or migrate your existing code to ONM, please get in touch with us.
Ways to Get Involved
Recent Workshops
Project Phasor - Kickoff
- Brian Anderson
- 2025, January 28
Brian Anderson and others will talk about the newly launched Project Phasor that aims to organize efforts towards neuromorphic and NeuroAI virtualization and compilation.
C-DNN and C-Transformer: mixing ANNs and SNNs for the best of both worlds
- Sangyeob Kim
- 2024, May 4
Join us for a talk by Sangyeob Kim, Postdoctoral researcher at KAIST, on designing efficient accelerators that mix SNNs and ANNs.
Advances in Neuromorphic Visual Place Recognition
- Tobias Fischer
- 2024, April 18
Tobias Fischer shares advances in neuromorphic visual place recognition.
Recent Posts
NorthPole, IBM's latest Neuromorphic AI Hardware
- Fabrizio Ottati
Translating the NorthPole paper from IBM to human language.
Spiking Neural Network (SNN) Library Benchmarks
- Gregor Lenz, Kade Heckel, Sumit Bam Shrestha, Cameron Barker, Jens Egholm Pedersen
Discover the fastest Spiking Neural Network (SNN) frameworks for deep learning-based optimization. Performance, flexibility, and more analyzed in-depth
TrueNorth: A Deep Dive into IBM's Neuromorphic Chip Design
- Fabrizio Ottati
Explore the innovative TrueNorth neuromorphic chip, its event-driven architecture, low power operation, massive parallelism, real-time capabilities, and scalable design.
Efficient Compression for Event-Based Data in Neuromorphic Applications
- Gregor Lenz, Fabrizio Ottati, Alexandre Marcireau
Discover methods to efficiently encode and store event-based data from high-resolution event cameras, striking a balance between file size and fast retrieval for spiking neural network training.