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Energy-efficient and high-bandwidth neuromorphic nanophotonic Chips for Artificial Intelligence systems

Project description

Portable, more efficient AI systems that work like human brains

Today’s most successful AI algorithms are inspired by brain-like neural networks. However, unlike our highly efficient brains, running these algorithms on computers consumes very large amounts of energy. These extremely inefficient central processing units hinder the development of efficient, scalable and portable AI systems. The EU-funded ChipAI project will tap into the potential of photonics nanotechnology to deliver compact, high-bandwidth and energy-efficient central processing units. Researchers will use resonant tunnelling semiconductor nanostructures embedded in sub-wavelength metal cavities 100 times smaller than conventional ones to efficiently confine, emit and detect light. Project results will pave the way for further developments in the emerging field of neuromorphic optical computing.

Objective

The same way the internet revolutionized our society, the rise of Artificial Intelligence (AI) that can learn without the need of explicit instructions is transforming our life. AI uses brain inspired neural network algorithms powered by computers. However, these central processing units (CPU) are extremely energy inefficient at implementing these tasks. This represents a major bottleneck for energy efficient, scalable and portable AI systems. Reducing the energy consumption of the massively dense interconnects in existing CPUs needed to emulate complex brain functions is a major challenge. ChipAI aims at developing a nanoscale photonics-enabled technology capable of deliver compact, high-bandwidth and energy efficiency CPUs using optically interconnected spiking neuron-like sources and detectors. ChipAI will pursue its main goal through the exploitation of Resonant Tunnelling (RT) semiconductor nanostructures embedded in sub-wavelength metal cavities, with dimensions 100 times smaller over conventional devices, for efficient light confinement, emission and detection. Key elements developed are non-linear RT nanoscale lasers, LEDs, detectors, and synaptic optical links on silicon substrates to make an economically viable technology. This platform will be able to fire and detect neuron-like light-spiking (pulsed) signals at rates 1 billion times faster than biological neurons (>10 GHz per spike rates) and requiring ultralow energy (<10 fJ). This radically new architecture will be tested for spike-encoding information processing towards validation for use in artificial neural networks. This will enable the development of real-time and offline portable AI and neuromorphic (brain-like) CPUs. In perspective, ChipAI will not only lay the foundations of the new field of neuromorphic optical computing, as will enable new non-AI functional applications in biosensing, imaging and many other fields where masses of cheap miniaturized pulsed sources and detectors are needed.

Call for proposal

H2020-FETOPEN-2018-2020

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Sub call

H2020-FETOPEN-2018-2019-2020-01

Coordinator

INTERNATIONAL IBERIAN NANOTECHNOLOGY LABORATORY
Net EU contribution
€ 653 625,00
Address
AVENIDA MESTRE JOSE VEIGA
4715-330 Braga
Portugal

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Region
Continente Norte Cávado
Activity type
Research Organisations
Links
Total cost
€ 653 625,00

Participants (8)