SpiNNaker 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.
Current Limitations
Up to now, our technology has been limited to large-scale systems on the cloud.
Project Goals
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. 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)
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automated in-line separatioN and dEtection of eXtracellular vesicles for liqUid biopsy applicationS
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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.
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HYBRAIN aims to develop a brain-inspired hybrid architecture combining integrated photonics and unconventional electronics for ultrafast, energy-efficient edge AI inference.
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SPIKEPro aims to develop an integrated neuromorphic chip combining electrical and photonic neurons to create efficient, high-speed spiking neural networks for diverse applications.
n-ary spintronics-based edge computing co-processor for artificial intelligence
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