SubsidieMeestersSPIKING 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.
Projectdetails
Introduction
Rapid advances in artificial intelligence technologies have led to powerful models and algorithms that have revolutionized many applications across all fields of science and technology. Deep learning performed within artificial neural networks has yielded new ways to process data, leading to sophisticated systems with impressive functionality and benefits.
Need for New Computing Hardware
However, conventional computing hardware is reaching its limits in terms of energy efficiency and speed. A new approach to computing hardware is needed. Novel brain-inspired or neuromorphic chips working with biologically-inspired spiking neural networks have gained attention as they promise highly efficient ways to process data.
Research Efforts
Important research effort has been dedicated to developing such neuromorphic systems in electronic or photonic hardware separately, each with its drawbacks and limitations.
SPIKEPro Proposal
SPIKEPro proposes a science-towards-technology breakthrough by combining low-energy electrical and photonic neurons into a joint spiking neural network on an integrated circuit. SPIKEPro’s chip integration approach is based on a common technology platform, connecting ultrafast laser optical neurons with efficient electrical spiking diodes through non-volatile synaptic weights.
Advantages of SPIKEPro
This enables the simultaneous capitalization on the advantages of both electronics and photonics to deliver efficient and high-speed spiking neural networks (SNNs) going beyond existing implementations.
Energy Efficiency and Learning Strategies
In addition to reducing the energy consumption per spike in the network, SPIKEPro will also develop novel learning strategies and algorithms able to work with a reduced number of synaptic connections. These will be possible by exploiting the hardware parameters of the electrical and photonic spiking devices.
Impact of SPIKEPro
The outcome of SPIKEPro will have lasting economic, societal, and scientific impact. The project will bring ultra-fast and efficient neuromorphic hardware into the disparate fields of:
- Edge computing
- Sensor data processing
- High-speed control
- Computational neuroscience
Financiële details & Tijdlijn
Financiële details
| Subsidiebedrag | € 1.973.038 |
| Totale projectbegroting | € 1.973.038 |
Tijdlijn
| Startdatum | 1-3-2024 |
| Einddatum | 29-2-2028 |
| Subsidiejaar | 2024 |
Partners & Locaties
Projectpartners
- TECHNISCHE UNIVERSITEIT EINDHOVENpenvoerder
- TECHNISCHE UNIVERSITAET ILMENAU
- HEWLETT PACKARD ENTERPRISE BELGIUM
- UNIVERSITY OF STRATHCLYDE
- UNIVERSITY COLLEGE LONDON
Land(en)
Vergelijkbare projecten binnen EIC Pathfinder
| Project | Regeling | Bedrag | Jaar | Actie |
|---|---|---|---|---|
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 |
RECONFIGURABLE SUPERCONDUTING AND PHOTONIC TECHNOLOGIES OF THE FUTURERESPITE aims to develop a compact, scalable neuromorphic computing platform integrating vision and cognition on a single chip using superconducting technologies for ultra-low power and high performance. | EIC Pathfinder | € 2.455.823 | 2023 | Details |
Nano electro-optomechanical programmable integrated circuitsNEUROPIC aims to develop a programmable photonic chip architecture for diverse applications, leveraging nanoelectromechanical technologies to enhance efficiency and enable neuromorphic computing. | EIC Pathfinder | € 2.999.924 | 2023 | Details |
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 |
Neuromorphic computing Enabled by Heavily doped semiconductor OpticsNEHO aims to create a novel photonic integrated circuit for ultrafast, low-energy neuromorphic processing using nonlinear photon-plasmon technology to enhance machine learning capabilities. | EIC Pathfinder | € 2.982.184 | 2023 | Details |
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.
RECONFIGURABLE SUPERCONDUTING AND PHOTONIC TECHNOLOGIES OF THE FUTURE
RESPITE aims to develop a compact, scalable neuromorphic computing platform integrating vision and cognition on a single chip using superconducting technologies for ultra-low power and high performance.
Nano electro-optomechanical programmable integrated circuits
NEUROPIC aims to develop a programmable photonic chip architecture for diverse applications, leveraging nanoelectromechanical technologies to enhance efficiency and enable neuromorphic computing.
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.
Neuromorphic computing Enabled by Heavily doped semiconductor Optics
NEHO aims to create a novel photonic integrated circuit for ultrafast, low-energy neuromorphic processing using nonlinear photon-plasmon technology to enhance machine learning capabilities.
Vergelijkbare projecten uit andere regelingen
| Project | Regeling | Bedrag | Jaar | Actie |
|---|---|---|---|---|
Perovskite Spiking Neurons for Intelligent NetworksThis project aims to develop compact perovskite-based devices that emulate neuron behavior for efficient spiking neural networks, enhancing perception and computation while reducing energy costs. | ERC Advanced... | € 2.498.004 | 2023 | Details |
SpiNNaker on the EdgeSpiNNcloud 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. | EIC Transition | € 2.499.998 | 2023 | Details |
Three dimensional INtegrated PhotonIcS to RevolutionizE deep LearningThis project aims to develop advanced photonic neural network processors to significantly enhance computational efficiency and scalability, revolutionizing AI hardware and applications. | ERC Consolid... | € 1.998.918 | 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 |
Spiking Control Systems: an algorithmic theory for control design of physical event-based systemsThis project aims to develop a control theory of spiking systems to create novel event-based design principles for neuromorphic devices, enhancing learning and adaptation beyond digital machines. | ERC Advanced... | € 2.498.741 | 2023 | Details |
Perovskite Spiking Neurons for Intelligent Networks
This project aims to develop compact perovskite-based devices that emulate neuron behavior for efficient spiking neural networks, enhancing perception and computation while reducing energy costs.
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.
Three dimensional INtegrated PhotonIcS to RevolutionizE deep Learning
This project aims to develop advanced photonic neural network processors to significantly enhance computational efficiency and scalability, revolutionizing AI hardware and applications.
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.
Spiking Control Systems: an algorithmic theory for control design of physical event-based systems
This project aims to develop a control theory of spiking systems to create novel event-based design principles for neuromorphic devices, enhancing learning and adaptation beyond digital machines.