Summary
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. 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. Important research effort has been dedicated to develop such neuromorphic systems in electronic or photonic hardware separately, each with its drawbacks and limitations. 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. This enables to simultaneously capitalise on the advantages of both electronics and photonics to deliver efficient and high-speed SNNs going beyond existing implementations. 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 reduced number of synaptic connections. These will be possible by exploiting the hardware parameters of the electrical and photonic spiking devices. 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 and computational neuroscience.
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| Web resources: | https://cordis.europa.eu/project/id/101129904 |
| Start date: | 01-03-2024 |
| End date: | 29-02-2028 |
| Total budget - Public funding: | 1 973 038,75 Euro - 1 973 038,00 Euro |
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Original description
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. 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. Important research effort has been dedicated to develop such neuromorphic systems in electronic or photonic hardware separately, each with its drawbacks and limitations. 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. This enables to simultaneously capitalise on the advantages of both electronics and photonics to deliver efficient and high-speed SNNs going beyond existing implementations. 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 reduced number of synaptic connections. These will be possible by exploiting the hardware parameters of the electrical and photonic spiking devices. 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 and computational neuroscience.Status
SIGNEDCall topic
HORIZON-EIC-2023-PATHFINDEROPEN-01-01Update Date
12-03-2024
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