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

Angelo Garofalo et al. (2021)

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DOI: 10.1109/ARITH51176.2021.00020

access: closed

type: Conference or Workshop Paper

metadata version: 2021-11-23

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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. ARITH 2021: 53

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