Zobrazeno 1 - 10
of 81
pro vyhledávání: '"Garofalo, Angelo"'
Autor:
Ciani, Maicol, Parisi, Emanuele, Musa, Alberto, Barchi, Francesco, Bartolini, Andrea, Kulmala, Ari, Psiakis, Rafail, Garofalo, Angelo, Acquaviva, Andrea, Rossi, Davide
The rapid advancement and exploration of open-hardware RISC-V platforms are driving significant changes in sectors like autonomous vehicles, smart-city infrastructure, and medical devices. OpenTitan stands out as a groundbreaking open-source RISC-V d
Externí odkaz:
http://arxiv.org/abs/2406.11558
Autor:
Liang, Chaoqun, Ottaviano, Alessandro, Benz, Thomas, Sinigaglia, Mattia, Benini, Luca, Garofalo, Angelo, Rossi, Davide
The ongoing revolution in application domains targeting autonomous navigation, first and foremost automotive "zonalization", has increased the importance of certain off-chip communication interfaces, particularly Ethernet. The latter will play an ess
Externí odkaz:
http://arxiv.org/abs/2406.06394
Autor:
Rogenmoser, Michael, Ottaviano, Alessandro, Benz, Thomas, Balas, Robert, Perotti, Matteo, Garofalo, Angelo, Benini, Luca
In the last decade, we have witnessed exponential growth in the complexity of control systems for safety-critical applications (automotive, robots, industrial automation) and their transition to heterogeneous mixed-criticality systems (MCSs). The gro
Externí odkaz:
http://arxiv.org/abs/2406.06546
Autor:
Benz, Thomas, Ottaviano, Alessandro, Balas, Robert, Garofalo, Angelo, Restuccia, Francesco, Biondi, Alessandro, Benini, Luca
The increasing demand for heterogeneous functionality in the automotive industry and the evolution of chip manufacturing processes have led to the transition from federated to integrated critical real-time embedded systems (CRTESs). This leads to hig
Externí odkaz:
http://arxiv.org/abs/2311.09662
Autor:
İslamoğlu, Gamze, Scherer, Moritz, Paulin, Gianna, Fischer, Tim, Jung, Victor J. B., Garofalo, Angelo, Benini, Luca
Transformer networks have emerged as the state-of-the-art approach for natural language processing tasks and are gaining popularity in other domains such as computer vision and audio processing. However, the efficient hardware acceleration of transfo
Externí odkaz:
http://arxiv.org/abs/2307.03493
Autor:
Nadalini, Alessandro, Rutishauser, Georg, Burrello, Alessio, Bruschi, Nazareno, Garofalo, Angelo, Benini, Luca, Conti, Francesco, Rossi, Davide
The emerging trend of deploying complex algorithms, such as Deep Neural Networks (DNNs), increasingly poses strict memory and energy efficiency requirements on Internet-of-Things (IoT) end-nodes. Mixed-precision quantization has been proposed as a te
Externí odkaz:
http://arxiv.org/abs/2307.01056
Autor:
Silvano, Cristina, Ielmini, Daniele, Ferrandi, Fabrizio, Fiorin, Leandro, Curzel, Serena, Benini, Luca, Conti, Francesco, Garofalo, Angelo, Zambelli, Cristian, Calore, Enrico, Schifano, Sebastiano Fabio, Palesi, Maurizio, Ascia, Giuseppe, Patti, Davide, Petra, Nicola, De Caro, Davide, Lavagno, Luciano, Urso, Teodoro, Cardellini, Valeria, Cardarilli, Gian Carlo, Birke, Robert, Perri, Stefania
Recent trends in deep learning (DL) imposed hardware accelerators as the most viable solution for several classes of high-performance computing (HPC) applications such as image classification, computer vision, and speech recognition. This survey summ
Externí odkaz:
http://arxiv.org/abs/2306.15552
Autor:
Sinigaglia, Mattia, Bertaccini, Luca, Valente, Luca, Garofalo, Angelo, Benatti, Simone, Benini, Luca, Conti, Francesco, Rossi, Davide
Emerging applications in the IoT domain require ultra-low-power and high-performance end-nodes to deal with complex near-sensor-data analytics. Domains such as audio, radar, and Structural Health Monitoring require many computations to be performed i
Externí odkaz:
http://arxiv.org/abs/2305.07325
DARKSIDE: A Heterogeneous RISC-V Compute Cluster for Extreme-Edge On-Chip DNN Inference and Training
Autor:
Garofalo, Angelo, Tortorella, Yvan, Perotti, Matteo, Valente, Luca, Nadalini, Alessandro, Benini, Luca, Rossi, Davide, Conti, Francesco
On-chip DNN inference and training at the Extreme-Edge (TinyML) impose strict latency, throughput, accuracy and flexibility requirements. Heterogeneous clusters are promising solutions to meet the challenge, combining the flexibility of DSP-enhanced
Externí odkaz:
http://arxiv.org/abs/2303.17954
Autor:
Ciani, Maicol, Bonato, Stefano, Psiakis, Rafail, Garofalo, Angelo, Valente, Luca, Sugumar, Suresh, Giusti, Alessandro, Rossi, Davide, Palossi, Daniele
Autonomous Micro Aerial Vehicles (MAVs), with a form factor of 10cm in diameter, are an emerging technology thanks to the broad applicability enabled by their onboard intelligence. However, these platforms are strongly limited in the onboard power en
Externí odkaz:
http://arxiv.org/abs/2303.16554