Zobrazeno 1 - 10
of 53
pro vyhledávání: '"Di Mauro, Alfio"'
Autor:
Potocnik, Viviane, Di Mauro, Alfio, Lamberti, Lorenzo, Kartsch, Victor, Scherer, Moritz, Conti, Francesco, Benini, Luca
Embodied artificial intelligence (AI) requires pushing complex multi-modal models to the extreme edge for time-constrained tasks such as autonomous navigation of robots and vehicles. On small form-factor devices, e.g., nano-sized unmanned aerial vehi
Externí odkaz:
http://arxiv.org/abs/2410.09054
Autor:
Prasad, Arpan Suravi, Scherer, Moritz, Conti, Francesco, Rossi, Davide, Di Mauro, Alfio, Eggimann, Manuel, Gómez, Jorge Tómas, Li, Ziyun, Sarwar, Syed Shakib, Wang, Zhao, De Salvo, Barbara, Benini, Luca
Extended reality (XR) applications are Machine Learning (ML)-intensive, featuring deep neural networks (DNNs) with millions of weights, tightly latency-bound (10-20 ms end-to-end), and power-constrained (low tens of mW average power). While ML perfor
Externí odkaz:
http://arxiv.org/abs/2312.14750
Autor:
Bian, Sizhen, Schulthess, Lukas, Rutishauser, Georg, Di Mauro, Alfio, Benini, Luca, Magno, Michele
The interest in dynamic vision sensor (DVS)-powered unmanned aerial vehicles (UAV) is raising, especially due to the microsecond-level reaction time of the bio-inspired event sensor, which increases robustness and reduces latency of the perception ta
Externí odkaz:
http://arxiv.org/abs/2305.18371
Autor:
Conti, Francesco, Paulin, Gianna, Rossi, Davide, Di Mauro, Alfio, Rutishauser, Georg, Ottavi, Gianmarco, Eggimann, Manuel, Okuhara, Hayate, Benini, Luca
Emerging Artificial Intelligence-enabled Internet-of-Things (AI-IoT) System-on-a-Chip (SoC) for augmented reality, personalized healthcare, and nano-robotics need to run many diverse tasks within a power envelope of a few tens of mW over a wide range
Externí odkaz:
http://arxiv.org/abs/2305.08415
Autor:
Rutishauser, Georg, Hunziker, Robin, Di Mauro, Alfio, Bian, Sizhen, Benini, Luca, Magno, Michele
End-to-end event-based computation has the potential to push the envelope in latency and energy efficiency for edge AI applications. Unfortunately, event-based sensors (e.g., DVS cameras) and neuromorphic spike-based processors (e.g., Loihi) have bee
Externí odkaz:
http://arxiv.org/abs/2302.07957
Tiny Machine Learning (TinyML) applications impose uJ/Inference constraints, with a maximum power consumption of tens of mW. It is extremely challenging to meet these requirements at a reasonable accuracy level. This work addresses the challenge with
Externí odkaz:
http://arxiv.org/abs/2212.00688
Autor:
Liao, Jiawei, Widmer, Lars, Wang, Xiaying, Di Mauro, Alfio, Nason-Tomaszewski, Samuel R., Chestek, Cynthia A., Benini, Luca, Jang, Taekwang
Publikováno v:
2022 IEEE 4th International Conference on Artificial Intelligence Circuits and Systems (AICAS), 2022, pp. 134-137
Brain-machine interfaces (BMIs) are promising for motor rehabilitation and mobility augmentation. High-accuracy and low-power algorithms are required to achieve implantable BMI systems. In this paper, we propose a novel spiking neural network (SNN) d
Externí odkaz:
http://arxiv.org/abs/2210.06287
Small-size unmanned aerial vehicles (UAV) have the potential to dramatically increase safety and reduce cost in applications like critical infrastructure maintenance and post-disaster search and rescue. Many scenarios require UAVs to shrink toward na
Externí odkaz:
http://arxiv.org/abs/2209.01065
Autor:
Di Mauro, Alfio, Prasad, Arpan Suravi, Huang, Zhikai, Spallanzani, Matteo, Conti, Francesco, Benini, Luca
Event-based sensors are drawing increasing attention due to their high temporal resolution, low power consumption, and low bandwidth. To efficiently extract semantically meaningful information from sparse data streams produced by such sensors, we pre
Externí odkaz:
http://arxiv.org/abs/2204.10687
Autor:
Ottavi, Gianmarco, Garofalo, Angelo, Tagliavini, Giuseppe, Conti, Francesco, Di Mauro, Alfio, Benini, Luca, Rossi, Davide
Computationally intensive algorithms such as Deep Neural Networks (DNNs) are becoming killer applications for edge devices. Porting heavily data-parallel algorithms on resource-constrained and battery-powered devices poses several challenges related
Externí odkaz:
http://arxiv.org/abs/2201.08656