SubsidieMeestersSpiNNaker on the Edge
SpiNNcloud Systems aims to develop real-time, energy-efficient AI applications by transitioning cutting-edge neuromorphic computing technology from cloud to edge, enhancing performance and commercialization.
Projectdetails
Introduction
AI driven systems are improving our lives tremendously, but they face a performance wall. They have to rely on hardware with communication bottlenecks that limit AI applications and waste a lot of energy.
Background
In 2013, Europe launched the billion-euro Human Brain Project to deepen our understanding of neuroscience and neuromorphic computing. SpiNNcloud Systems is a deep-tech spinoff leveraging cutting-edge research backed by thousands of publications from the Human Brain Project to provide highly-parallel and real-time computing solutions that can power the third generation of AI-driven systems.
Technology Overview
Our unprecedented SpiNNcloud platform presents a unique combination of:
- Neuromorphic layers
- Deep Learning layers
- Symbolic layers
These layers yield real-time, low-latency, energy-efficient, and cognitive AI systems.
Project Goals
Up to now, our technology has been limited to large-scale systems on the cloud. Our project will enable us to develop the SpiNNode edge system so that AI applications can run on the cloud to edge continuum in real time.
Commercialization Path
This will not only increase the visibility of SpiNNaker2 technology, but it will also set up a new path for commercializing our core technology.
Support and Mission
With EIC Transition support, we will translate cutting-edge science originating from the Human Brain Project into solid industrial applications. Our mission is to be the European pioneer driving the third wave of AI: real-time, autonomous, and sustainable.
Financiële details & Tijdlijn
Financiële details
| Subsidiebedrag | € 2.499.998 |
| Totale projectbegroting | € 2.499.998 |
Tijdlijn
| Startdatum | 1-5-2023 |
| Einddatum | 30-4-2025 |
| Subsidiejaar | 2023 |
Partners & Locaties
Projectpartners
- SPINNCLOUD SYSTEMS GMBHpenvoerder
Land(en)
Vergelijkbare projecten binnen EIC Transition
| Project | Regeling | Bedrag | Jaar | Actie |
|---|---|---|---|---|
Hybrid Spintronic Synapses for Neuromorphic ComputingSpin-Ion Technologies aims to develop neuromorphic chips using ion beam-engineered magnetic materials, bridging computational neuroscience and deep learning for efficient embedded systems. | EIC Transition | € 2.499.998 | 2023 | Details |
Magnetic neural Network for predictive maintenanceGolana Computing aims to develop bio-mimicking magnetic neurons for real-time analog signal analysis, enhancing predictive maintenance in manufacturing while minimizing energy consumption. | EIC Transition | € 2.499.999 | 2023 | Details |
Hybrid Spintronic Synapses for Neuromorphic Computing
Spin-Ion Technologies aims to develop neuromorphic chips using ion beam-engineered magnetic materials, bridging computational neuroscience and deep learning for efficient embedded systems.
Magnetic neural Network for predictive maintenance
Golana Computing aims to develop bio-mimicking magnetic neurons for real-time analog signal analysis, enhancing predictive maintenance in manufacturing while minimizing energy consumption.
Vergelijkbare projecten uit andere regelingen
| Project | Regeling | Bedrag | Jaar | Actie |
|---|---|---|---|---|
n-ary spintronics-based edge computing co-processor for artificial intelligenceMultiSpin.AI aims to revolutionize edge computing by developing a neuromorphic AI co-processor that enhances energy efficiency and processing speed, enabling transformative applications while reducing CO2 emissions. | EIC Pathfinder | € 3.143.276 | 2024 | Details |
SPIKING PHOTONIC-ELECTRONIC IC FOR QUICK AND EFFICIENT PROCESSINGSPIKEPro aims to develop an integrated neuromorphic chip combining electrical and photonic neurons to create efficient, high-speed spiking neural networks for diverse applications. | EIC Pathfinder | € 1.973.038 | 2024 | Details |
A novel hardware & software platform to revolutionise artificial intelligence at the edgeDeveloping a scalable hardware and software platform for efficient edge AI inference, targeting computer vision and NLP, to drive adoption with reduced costs and power consumption. | EIC Accelerator | € 2.499.999 | 2024 | Details |
Hybrid electronic-photonic architectures for brain-inspired computingHYBRAIN aims to develop a brain-inspired hybrid architecture combining integrated photonics and unconventional electronics for ultrafast, energy-efficient edge AI inference. | EIC Pathfinder | € 1.672.528 | 2022 | Details |
Memristive Neurons and Synapses for Neuromorphic Edge ComputingMEMRINESS aims to develop compact, power-efficient Spiking Neural Networks using memristive technology for enhanced collaborative learning on edge systems. | ERC Starting... | € 1.499.488 | 2022 | Details |
n-ary spintronics-based edge computing co-processor for artificial intelligence
MultiSpin.AI aims to revolutionize edge computing by developing a neuromorphic AI co-processor that enhances energy efficiency and processing speed, enabling transformative applications while reducing CO2 emissions.
SPIKING PHOTONIC-ELECTRONIC IC FOR QUICK AND EFFICIENT PROCESSING
SPIKEPro aims to develop an integrated neuromorphic chip combining electrical and photonic neurons to create efficient, high-speed spiking neural networks for diverse applications.
A novel hardware & software platform to revolutionise artificial intelligence at the edge
Developing a scalable hardware and software platform for efficient edge AI inference, targeting computer vision and NLP, to drive adoption with reduced costs and power consumption.
Hybrid electronic-photonic architectures for brain-inspired computing
HYBRAIN aims to develop a brain-inspired hybrid architecture combining integrated photonics and unconventional electronics for ultrafast, energy-efficient edge AI inference.
Memristive Neurons and Synapses for Neuromorphic Edge Computing
MEMRINESS aims to develop compact, power-efficient Spiking Neural Networks using memristive technology for enhanced collaborative learning on edge systems.