Archivio Istituzionale della RicercaThe shift of Convolutional Neural Networks (ConvNets) into low-power devices with limited compute and memory resources calls for cross-layer strategies spanning from hardware to software optimization. This work answers to this need, presenting a collection of tools for efficient deployment of ConvNets on the edge.
Optimization Tools for ConvNets on the Edge / Peluso, V.; Macii, E.; Calimera, A.. - ELETTRONICO. - (2020), pp. 204-205. (Intervento presentato al convegno 28th IFIP/IEEE International Conference on Very Large Scale Integration, VLSI-SOC 2020 tenutosi a Salt Lake City, UT, USA nel 2020) [10.1109/VLSI-SOC46417.2020.9344075].
Optimization Tools for ConvNets on the Edge
Peluso V.;MacIi E.;Calimera A.
2020
Abstract
The shift of Convolutional Neural Networks (ConvNets) into low-power devices with limited compute and memory resources calls for cross-layer strategies spanning from hardware to software optimization. This work answers to this need, presenting a collection of tools for efficient deployment of ConvNets on the edge.
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https://hdl.handle.net/11583/2957350