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"XpulpNN: Enabling Energy Efficient and Flexible Inference of Quantized ..."

Angelo Garofalo et al. (2021)

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DOI: 10.1109/TETC.2021.3072337

access: closed

type: Journal Article

metadata version: 2021-10-05

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Angelo Garofalo, Giuseppe Tagliavini, Francesco Conti, Luca Benini, Davide Rossi:
XpulpNN: Enabling Energy Efficient and Flexible Inference of Quantized Neural Networks on RISC-V Based IoT End Nodes. IEEE Trans. Emerg. Top. Comput. 9(3): 1489-1505 (2021)

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