Search Results for author: Danilo Pau

Found 6 papers, 2 papers with code

Resource Constrained Neural Networks for 5G Direction-of-Arrival Estimation in Micro-controllers

no code implementations23 Jul 2021 Piyush Sahoo, Romesh Rajoria, Shivam Chandhok, S. J. Darak, Danilo Pau, Hem-Dutt Dabral

With the introduction of shared spectrum sensing and beam-forming based multi-antenna transceivers, 5G networks demand spectrum sensing to identify opportunities in time, frequency, and spatial domains.

Direction of Arrival Estimation

Characterization of Neural Networks Automatically Mapped on Automotive-grade Microcontrollers

no code implementations27 Feb 2021 Giulia Crocioni, Giambattista Gruosso, Danilo Pau, Davide Denaro, Luigi Zambrano, Giuseppe di Giore

Nowadays, Neural Networks represent a major expectation for the realization of powerful Deep Learning algorithms, which can determine several physical systems' behaviors and operations.

Capacity Estimation Intrusion Detection

Benchmarking TinyML Systems: Challenges and Direction

1 code implementation10 Mar 2020 Colby R. Banbury, Vijay Janapa Reddi, Max Lam, William Fu, Amin Fazel, Jeremy Holleman, Xinyuan Huang, Robert Hurtado, David Kanter, Anton Lokhmotov, David Patterson, Danilo Pau, Jae-sun Seo, Jeff Sieracki, Urmish Thakker, Marian Verhelst, Poonam Yadav

In this position paper, we present the current landscape of TinyML and discuss the challenges and direction towards developing a fair and useful hardware benchmark for TinyML workloads.


Reduced Memory Region Based Deep Convolutional Neural Network Detection

no code implementations8 Sep 2016 Denis Tome', Luca Bondi, Emanuele Plebani, Luca Baroffio, Danilo Pau, Stefano Tubaro

Accurate pedestrian detection has a primary role in automotive safety: for example, by issuing warnings to the driver or acting actively on car's brakes, it helps decreasing the probability of injuries and human fatalities.

Pedestrian Detection

A Multi-signal Variant for the GPU-based Parallelization of Growing Self-Organizing Networks

no code implementations28 Mar 2015 Giacomo Parigi, Angelo Stramieri, Danilo Pau, Marco Piastra

Among the many possible approaches for the parallelization of self-organizing networks, and in particular of growing self-organizing networks, perhaps the most common one is producing an optimized, parallel implementation of the standard sequential algorithms reported in the literature.

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