Project description
Edge computing inspired by neuroscience is ultra energy-efficient and secure
The global network of internet-connected devices is expanding exponentially. Endpoint devices at the "edge" of the IoT network are increasingly requested to perform advanced sensing and processing, in particular AI inference, to extract valuable information from their environments and eliminate the need to transfer vast amounts of raw data to cloud servers. This is important in many consumer applications to improve user satisfaction, as well as in safety-critical applications such as self-driving cars. Enhanced sensing and processing at the "edge" reduces latencies and data degradation while enhancing security and efficiency over the entire IoT network. The EU-funded NimbleAI project will create an integral neuromorphic sensing–processing architecture to efficiently run accurate and diverse computer vision algorithms in resource- and area-constrained chips destined for endpoint devices.
Objective
Today only very light AI processing tasks are executed in ubiquitous IoT endpoint devices, where sensor data are generated and access to energy is usually constrained. However, this approach is not scalable and results in high penalties in terms of security, privacy, cost, energy consumption, and latency as data need to travel from endpoint devices to remote processing systems such as data centres. Inefficiencies are especially evident in energy consumption.
To keep up pace with the exponentially growing amount of data (e.g. video) and allow more advanced, accurate, safe and timely interactions with the surrounding environment, next-generation endpoint devices will need to run AI algorithms (e.g. computer vision) and other compute intense tasks with very low latency (i.e. units of ms or less) and energy envelops (i.e. tens of mW or less).
NimbleAI will harness the latest advances in microelectronics and integrated circuit technology to create an integral neuromorphic sensing-processing solution to efficiently run accurate and diverse computer vision algorithms in resource- and area-constrained chips destined to endpoint devices. Biology will be a major source of inspiration in NimbleAI, especially with a focus to reproduce adaptivity and experience-induced plasticity that allow biological structures to continuously become more efficient in processing dynamic visual stimuli.
NimbleAI is expected to allow significant improvements compared to state-of-the-art (e.g. commercially available neuromorphic chips), and at least 100x improvement in energy efficiency and 50x shorter latency compared to state-of-the-practice (e.g. CPU/GPU/NPU/TPUs processing frame-based video). NimbleAI will also take a holistic approach for ensuring safety and security at different architecture levels, including silicon level.
Fields of science
- natural sciencescomputer and information sciencesinternetinternet of things
- natural sciencescomputer and information sciencesartificial intelligencecomputer vision
- engineering and technologyelectrical engineering, electronic engineering, information engineeringelectronic engineeringsensors
- natural sciencesphysical scienceselectromagnetism and electronicsmicroelectronics
Keywords
Programme(s)
Funding Scheme
HORIZON-RIA - HORIZON Research and Innovation ActionsCoordinator
20500 Mondragon
Spain
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Participants (16)
08034 Barcelona
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06560 Valbonne
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The organization defined itself as SME (small and medium-sized enterprise) at the time the Grant Agreement was signed.
2311 EZ Leiden
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602 00 BRNO
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The organization defined itself as SME (small and medium-sized enterprise) at the time the Grant Agreement was signed.
5656AG Eindhoven
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The organization defined itself as SME (small and medium-sized enterprise) at the time the Grant Agreement was signed.
28006 Madrid
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46022 Valencia
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00197 Roma Rm
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The organization defined itself as SME (small and medium-sized enterprise) at the time the Grant Agreement was signed.
20133 Milano
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75015 PARIS 15
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3001 Leuven
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24118 Kiel
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The organization defined itself as SME (small and medium-sized enterprise) at the time the Grant Agreement was signed.
8020 Graz
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20560 Onati
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The organization defined itself as SME (small and medium-sized enterprise) at the time the Grant Agreement was signed.
1010 Wien
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The organization defined itself as SME (small and medium-sized enterprise) at the time the Grant Agreement was signed.
1040 Wien
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Partners (2)
M13 9PL Manchester
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E1 4NS London
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