NimbleAI: Towards Neuromorphic Sensing-Processing 3D-integrated Chips

Xabier Iturbe, Nassim Abderrahmane, Jaume Abella, Sergi Alcaide, Eric Beyne, Henri-Pierre Charles, Christelle Charpin-Nicolle, Lars Chittka, Angélica Dávila, Arne Erdmann, Carles Estrada, Ander Fernández, Anna Fontanelli, José Flich, Gianluca Furano, Alejandro Hernán Gloriani, Erik Isusquiza, Radu Grosu, Carles Hernández, Daniele Ielmini et al. (29 additional authors not shown)

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 $\mathbf{33D}$ 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) $\mathbf{100x}$ performance per mW gains compared to state-of-the-practice solutions (i.e., CPU/GPUs processing frame-based video); (2) $\mathbf{50x}$ 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 .