Zobrazeno 1 - 10
of 10 911
pro vyhledávání: '"Bosio A"'
Autor:
Chen, Tony G., Newdick, Stephanie, Di, Julia, Bosio, Carlo, Ongole, Nitin, Lapotre, Mathieu, Pavone, Marco, Cutkosky, Mark R.
Publikováno v:
Science Robotics 2024
Caves and lava tubes on the Moon and Mars are sites of geological and astrobiological interest but consist of terrain that is inaccessible with traditional robot locomotion. To support the exploration of these sites, we present ReachBot, a robot that
Externí odkaz:
http://arxiv.org/abs/2407.00973
Autor:
de Queiroz, Mauricio Gomes, Jimenez, Paul, Cardoso, Raphael, Costa, Mateus Vidaletti, Abdalla, Mohab, O'Connor, Ian, Bosio, Alberto, Pavanello, Fabio
Photonic Neural Networks (PNNs) are gaining significant interest in the research community due to their potential for high parallelization, low latency, and energy efficiency. PNNs compute using light, which leads to several differences in implementa
Externí odkaz:
http://arxiv.org/abs/2406.18757
Autor:
Barucci, Andrea, Ciacci, Giulia, Liò, Pietro, Azevedo, Tiago, Di Cencio, Andrea, Merella, Marco, Bianucci, Giovanni, Bosio, Giulia, Casati, Simone, Collareta, Alberto
All fields of knowledge are being impacted by Artificial Intelligence. In particular, the Deep Learning paradigm enables the development of data analysis tools that support subject matter experts in a variety of sectors, from physics up to the recogn
Externí odkaz:
http://arxiv.org/abs/2405.04189
Autor:
Taheri, Mahdi, Daneshtalab, Masoud, Raik, Jaan, Jenihhin, Maksim, Pappalardo, Salvatore, Jimenez, Paul, Deveautour, Bastien, Bosio, Alberto
Systolic array has emerged as a prominent architecture for Deep Neural Network (DNN) hardware accelerators, providing high-throughput and low-latency performance essential for deploying DNNs across diverse applications. However, when used in safety-c
Externí odkaz:
http://arxiv.org/abs/2403.02946
Autor:
Shahroodi, Taha, Cardoso, Raphael, Wong, Stephan, Bosio, Alberto, O'Connor, Ian, Hamdioui, Said
State-of-the-Art (SotA) hardware implementations of Deep Neural Networks (DNNs) incur high latencies and costs. Binary Neural Networks (BNNs) are potential alternative solutions to realize faster implementations without losing accuracy. In this paper
Externí odkaz:
http://arxiv.org/abs/2401.17724
Automated Layout Design and Control of Robust Cooperative Grasped-Load Aerial Transportation Systems
We present a novel approach to cooperative aerial transportation through a team of drones, using optimal control theory and a hierarchical control strategy. We assume the drones are connected to the payload through rigid attachments, essentially tran
Externí odkaz:
http://arxiv.org/abs/2310.07649
Autor:
Gashi, Triera, Bosio, Sophie Adeline Solheim, Kristensen, Joachim Tilsted, Thomsen, Michael Kirkedal
Property-based testing is a powerful method to validate program correctness. It is, however, not widely use in industry as the barrier of entry can be very high. One of the hindrances is to write the generators that are needed to generate randomised
Externí odkaz:
http://arxiv.org/abs/2309.04696
Autor:
Ahmadilivani, Mohammad Hasan, Barbareschi, Mario, Barone, Salvatore, Bosio, Alberto, Daneshtalab, Masoud, Della Torca, Salvatore, Gavarini, Gabriele, Jenihhin, Maksim, Raik, Jaan, Ruospo, Annachiara, Sanchez, Ernesto, Taheri, Mahdi
Deep Learning, and in particular, Deep Neural Network (DNN) is nowadays widely used in many scenarios, including safety-critical applications such as autonomous driving. In this context, besides energy efficiency and performance, reliability plays a
Externí odkaz:
http://arxiv.org/abs/2306.04645
Autor:
Pavanello, Fabio, Vatajelu, Elena Ioana, Bosio, Alberto, Van Vaerenbergh, Thomas, Bienstman, Peter, Charbonnier, Benoit, Carpegna, Alessio, Di Carlo, Stefano, Savino, Alessandro
Publikováno v:
2023 IEEE 41st VLSI Test Symposium (VTS)
The field of neuromorphic computing has been rapidly evolving in recent years, with an increasing focus on hardware design and reliability. This special session paper provides an overview of the recent developments in neuromorphic computing, focusing
Externí odkaz:
http://arxiv.org/abs/2305.01818
Deep learning techniques have become one of the main propellers for solving engineering problems effectively and efficiently. For instance, Predictive Maintenance methods have been used to improve predictions of when maintenance is needed on differen
Externí odkaz:
http://arxiv.org/abs/2301.12467