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Intel Neuromorphic Research Community

Accelerating research and adoption of breakthrough AI systems

The Intel Neuromorphic Research Community (Intel NRC) is an ecosystem of academic groups, government labs, research institutions, and companies around the world working with Intel to further neuromorphic computing and develop innovative AI applications.

 


Our Focus

Accelerate Research

With Intel's near-commercial neuromorphic computing systems, Intel NRC members have the tools to develop and test proof-of-concepts that collaboratively advance the field of neuromorphic computing.

Enable Commercial Applications

The Intel NRC is accelerating the adoption of neuromorphic technology by developing, prototyping, and scaling applications built on Intel's neuromorphic systems.

Open Benchmarking

As neuromorphic computing progresses toward commercialization, the Intel NRC is committed to setting benchmarks to measure the technology's value in an open, collaborative way.


What We Offer

Vibrant Research Ecosystem

The Intel NRC offers access to a global network of researchers that regularly share insights from their work to collaboratively break through challenges and advance the field.

Access to Small and Large-Scale Neuromorphic Systems

The Intel NRC provides members cloud-based access to both small and large-scale neuromorphic computing systems to further development of applications with impact from the edge to the data center.

Academic Funding

The Intel NRC offers funding for universities around the world to pursue their research plans.


Leading Together

The Intel Neuromorphic Research Community is a global network of more than 75 research groups who are committed to delivering on the promise of neuromorphic computing to make the technology a commercial reality.

With the Loihi chip we've been able to demonstrate 109 times lower power consumption running a real-time deep learning benchmark compared to a GPU, and 5 times lower power consumption compared to specialized IoT inference hardware. Even better, as we scale the network up by 50 times, Loihi maintains real-time performance results and uses only 30 percent more power, whereas the IoT hardware uses 500 percent more power and is no longer real-time.

Chris Eliasmith
Co-CEO of Applied Brain Research and Professor at University of Waterloo

Intel's Loihi neuromorphic processors have enormous potential to deliver new capabilities in AI and Edge computing. The flexibility in programming, ready access to the cloud-based resources and connections to a robust third-party neuromorphic computing ecosystem are all key factors industrial companies, like GE, require to transform complex industrial systems and networks.

Joel Markham
Chief Engineer of the Edge Computing Lab at GE Research

Loihi allowed us to realize a spiking neural network that imitates the brain's underlying neural representations and behavior. The SLAM solution emerged as a property of the network's structure. We benchmarked the Loihi-run network and found it to be equally accurate while consuming 100 times less energy than a widely used CPU-run SLAM method for mobile robots.

Konstantinos Michmizos
Professor at Rutgers University
Show more Show less View all


Select Publications

Loihi: A Neuromorphic Processor with On-Chip Learning

Loihi is a 60-mm 2 chip fabricated in Intel's 14-nm process that advances the state-of-the-art modeling of spiking neural networks in silicon. It integrates a wide range of novel features for the field, such as hierarchical connectivity, dendritic compartments, synaptic delays, and, most importantly, programmable synaptic learning rules. Running a spiking convolutional form of the Locally Competitive Algorithm, Loihi can...
  • Read article

Spiking Neural Network on Neuromorphic Hardware for Energy-Efficient Unidimensional SLAM

G. Tang, A. Shah, K. Michmizos (Rutgers)

Energy-efficient simultaneous localization and mapping (SLAM) is crucial for mobile robots exploring unknown environments. The mammalian brain solves SLAM via a network of specialized neurons, exhibiting asynchronous computations and...
  • Read article

Benchmarking Keyword Spotting Efficiency on Neuromorphic Hardware

P. Blouw, X. Choo, E. Hunsberger, C. Eliasmith (ABR)

Using Intel's Loihi neuromorphic research chip and ABR's Nengo Deep Learning toolkit, we analyze the inference speed, dynamic power consumption, and energy cost per inference of a two-layer neural network keyword spotter trained to recognize a single phrase. We perform comparative analyses of this keyword spotter running on more conventional hardware devices including...
  • Read article

Rapid Online Learning and Robust Recall in a Neuromorphic Olfactory Circuit

N. Imam (Intel), T. Cleland (Cornell)

We present a neural algorithm for the rapid online learning and identification of odorant samples under noise, based on the architecture of the mammalian olfactory bulb and implemented on the Intel Loihi neuromorphic system. As with biological olfaction, the spike timing-based algorithm utilizes distributed, event-driven computations and...
  • Read article

High Speed Cognitive Domain Ontologies for Asset Allocation Using Loihi Spiking Neurons

C. Yakopcic, T. Atahary, N. Rahman, T. M. Taha, A. Beigh, and S. Douglass

Cognitive agents are typically utilized in autonomous systems for automated decision making. These systems interact at real time with their environment and are generally heavily power constrained. Thus, there is a strong need for a real time agent running on a low power platform. The agent...
  • Read article

Robust Computation with Rhythmic Spike Patterns

EP Frady and F. Sommer (Intel/Berkeley)

This work makes 2 contributions. First, we present a neural network model of associative memory that stores and retrieves sparse patterns of complex variables. This network can store analog information as fixed-point attractors in the complex domain; it is governed by an energy function and has increased memory capacity compared to early models. Second, we...
  • Read article


In The News

Explore the latest news on Intel and neuromorphic computing.

Learn more


Join Us

 

Membership in the Intel Neuromorphic Research Community is open to all qualified academic, corporate, and government research groups around the world, at no charge. If you are interested in joining the Intel NRC, please email us introducing yourself and your research interests.

 

While members are encouraged to share the code, algorithms, and designs they develop using Intel's neuromorphic platforms, free sharing of intellectual property is not required. One of Intel's long-term goals is to nurture a commercial neuromorphic ecosystem, and as such, members may retain full proprietary ownership of any inventions that come from their work with Loihi.

 


Gallery

Members of the Intel NRC

Members of the Intel Neuromorphic Research Community share research progress and results at the group’s fall workshop in Graz, Austria. (Credit: Intel Corporation)

Loihi

Intel's self-learning neuromorphic research chip, codenamed Loihi. Loihi serves as the foundation for all neuromorphic systems available to the Intel NRC. (Credit: Intel Corporation)

Automated Foosball

Researchers at the 2019 Telluride Neuromorphic Cognition Engineering Workshop worked on automating Western Sydney University’s foosball table under Loihi control, operating on visual input from event-based cameras. Foosball offers an excellent test for rapid closed-loop sensing, planning, and control algorithms, a sweet spot for neuromorphic technology. (Credit: Sumit Bam Shrestha)

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