no code implementations • 26 Sep 2022 • Renzo Andri, Beatrice Bussolino, Antonio Cipolletta, Lukas Cavigelli, Zhe Wang
The Winograd-enhanced DSA achieves up to 1. 85x gain in energy efficiency and up to 1. 83x end-to-end speed-up for state-of-the-art segmentation and detection networks.
no code implementations • 7 Mar 2022 • Matteo Grimaldi, Luca Mocerino, Antonio Cipolletta, Andrea Calimera
This work introduces a new training and compression pipeline to build Nested Sparse ConvNets, a class of dynamic Convolutional Neural Networks (ConvNets) suited for inference tasks deployed on resource-constrained devices at the edge of the Internet-of-Things.