Additive Noise Annealing and Approximation Properties of Quantized Neural Networks. - Matteo Spallanzani; Lukas Cavigelli; Gian Paolo Leonardi; Marko Bertogna; Luca Benini (CoRR, 2019)

Analytical aspects of non-differentiable neural networks. - Gian Paolo Leonardi; Matteo Spallanzani (CoRR, 2020)

Proceedings of the DATE Friday Workshop on System-level Design Methods for Deep Learning on Heterogeneous Architectures (SLOHA 2021). - Frank Hannig; Paolo Meloni; Matteo Spallanzani; Matthias Ziegler 001 (CoRR, 2021)

Reducing neural architecture search spaces with training-free statistics and computational graph clustering. - Thorir Mar Ingolfsson; Mark Vero; Xiaying Wang; Lorenzo Lamberti; Luca Benini; Matteo Spallanzani (CF, 2022)

Training Quantised Neural Networks with STE Variants: the Additive Noise Annealing Algorithm. - Matteo Spallanzani; Gian Paolo Leonardi; Luca Benini (CVPR, 2022)

SNE: an Energy-Proportional Digital Accelerator for Sparse Event-Based Convolutions. - Alfio Di Mauro; Arpan Suravi Prasad; Zhikai Huang; Matteo Spallanzani; Francesco Conti 001; Luca Benini (DATE, 2022)

A fingerprint of a heterogeneous data set. - Matteo Spallanzani; Gueorgui Mihaylov; Marco Prato; Roberto Fontana (Adv. Data Anal. Classif., 2022)

ZippyPoint: Fast Interest Point Detection, Description, and Matching through Mixed Precision Discretization. - Menelaos Kanakis; Simon Maurer; Matteo Spallanzani; Ajad Chhatkuli; Luc Van Gool (CVPR Workshops, 2023)