XpulpNN: Enabling Energy Efficient and Flexible Inference of Quantized Neural Networks on RISC-V based IoT End Nodes

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Authors: Angelo Garofalo, Giuseppe Tagliavini, Francesco Conti, Luca Benini and Davide Rossi

Journal title: IEEE Transactions on Emerging Topics in Computing

Journal publisher: Institute of Electrical and Electronics Engineers

Published year: 2021

DOI identifier: 10.1109/tetc.2021.3072337

ISSN: 0890-8044