Summary
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.
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.
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More information & hyperlinks
| Web resources: | https://cordis.europa.eu/project/id/101070679 |
| Start date: | 01-10-2022 |
| End date: | 30-09-2025 |
| Total budget - Public funding: | 8 822 235,75 Euro - 8 822 235,00 Euro |
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Original description
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.
Status
SIGNEDCall topic
HORIZON-CL4-2021-DIGITAL-EMERGING-01-01Update Date
09-02-2023
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Organisations
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| Ikerlan (Coördinator)
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| AGENCIA ESTATAL CONSEJO SUPERIOR DE INVESTIGACIONES CIENTIFICAS
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| AVL LIST GMBH
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| BARCELONA SUPERCOMPUTING CENTER - CENTRO NACIONAL DE SUPERCOMPUTACION (BSC)
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| CODASIP S R O
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| COMMISSARIAT A L ENERGIE ATOMIQUE ET AUX ENERGIES ALTERNATIVES (CEA)
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| GRAI MATTER LABS BV (GrAI Matter Lab)
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| INTERUNIVERSITAIR MICRO-ELECTRONICA CENTRUM VZW
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| MENTA SAS
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| MONOZUKURI - SOCIETA' PER AZIONI
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| Politecnico di Milano
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| QUEEN MARY UNIVERSITY OF LONDON
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| RAYTRIX GMBH
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| SNAP B.V.
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| TECHNISCHE UNIVERSITAET WIEN