Institute of Electrical and Electronics Engineers (IEEE)
Doi
Abstract
The NimbleAI Horizon Europe project leverages key principles of energy-efficient visual sensing and processing in biological eyes and brains, and harnesses the latest advances in 33D stacked silicon integration, to create an integral sensing-processing neuromorphic architecture that efficiently and accurately runs computer vision algorithms in area-constrained endpoint chips. The rationale behind the NimbleAI architecture is: sense data only with high information value and discard data as soon as they are found not to be useful for the application (in a given context). The NimbleAI sensing-processing architecture is to be specialized after-deployment by tunning system-level trade-offs for each particular computer vision algorithm and deployment environment. The objectives of NimbleAI are: (1) 100x performance per mW gains compared to state-of-the-practice solutions (i.e., CPU/GPUs processing frame-based video); (2) 50x processing latency reduction compared to CPU/GPUs; (3) energy consumption in the order of tens of mWs; and (4) silicon area of approx. 50 mm 2 .NimbleAI has received funding from the EU’s Horizon Europe Research and Innovation programme (Grant Agreement 101070679), and by the UK Research and Innovation (UKRI) under the UK government’s Horizon Europe funding guarantee (Grant Agreement 10039070)Peer ReviewedArticle signat per 49 autors/es: Xabier Iturbe, IKERLAN, Basque Country (Spain); Nassim Abderrahmane, MENTA, France; Jaume Abella, Barcelona Supercomputing Center (BSC), Catalonia, Spain; Sergi Alcaide, Barcelona Supercomputing Center (BSC), Catalonia, Spain; Eric Beyne, IMEC, Belgium; Henri-Pierre Charles, CEA-LIST, University Grenoble Alpes, France; Christelle Charpin-Nicolle, CEALETI, Univ. Grenoble Alpes, France; Lars Chittka, Queen Mary University of London, UK; Angélica Dávila, IKERLAN, Basque Country (Spain); Arne Erdmann, Raytrix, Germany; Carles Estrada, IKERLAN, Basque Country (Spain); Ander Fernández, IKERLAN, Basque Country (Spain); Anna Fontanelli, Monozukuri (MZ Technologies), Italy; José Flich, Universitat Politecnica de Valencia, Spain; Gianluca Furano, ESA ESTEC, Netherlands; Alejandro Hernán Gloriani, Viewpointsystem, Austria; Erik Isusquiza, ULMA Medical Technologies, Basque Country (Spain); Radu Grosu, TU Wien, Austria; Carles Hernández, Universitat Politecnica de Valencia, Spain; Daniele Ielmini, Politecnico Milano, Italy; David Jackson, University of Manchester, UK; Maha Kooli, CEA-LIST, University Grenoble Alpes, France; Nicola Lepri, Politecnico Milano, Italy; Bernabé Linares-Barranco, CSIC, Spain; Jean-Loup Lachese, MENTA, France; Eric Laurent, MENTA, France; Menno Lindwer, GrAI Matter Labs (GML), Netherlands; Frank Linsenmaier, Viewpointsystem, Austria; Mikel Luján, University of Manchester, UK; Karel Masařík, CODASIP, Czech Republic; Nele Mentens, Universiteit Leiden, Netherlands; Orlando Moreira, GrAI Matter Labs (GML), Netherlands; Chinmay Nawghane, IMEC, Belgium; Luca Peres, University of Manchester, UK; Jean-Philippe Noel, CEA-LIST, University Grenoble Alpes, France; Arash Pourtaherian, GrAI Matter Labs (GML), Netherlands; Christoph Posch, PROPHESEE, France; Peter Priller, AVL List, Austria; Zdenek Prikryl, CODASIP, Czech Republic; Felix Resch, TU Wien, Austria; Oliver Rhodes, University of Manchester, UK; Todor Stefanov, Universiteit Leiden, Netherlands; Moritz Storring, IMEC, Belgium; Michele Taliercio, Monozukuri (MZ Technologies), Italy; Rafael Tornero, Universitat Politecnica de Valencia, Spain; Marcel van de Burgwal, IMEC, Belgium; Geert van der Plas, IMEC, Belgium; Elisa Vianello, CEALETI, Univ. Grenoble Alpes, France; Pavel Zaykov, CODASIP, Czech RepublicPostprint (author's final draft
