Zobrazeno 1 - 10
of 2 459
pro vyhledávání: '"BENINI, LUCA"'
Fine-tuning large-scale text-to-image diffusion models for various downstream tasks has yielded impressive results. However, the heavy computational burdens of tuning large models prevent personal customization. Recent advances have attempted to empl
Externí odkaz:
http://arxiv.org/abs/2410.21759
Autor:
Wessner, Marc-Andre, Villani, Federico, Papa, Sofia, Keller, Kirill, Ferrari, Laura, Greco, Francesco, Benini, Luca, Leitner, Christoph
Accurate characterization of ferroelectric properties in polymer piezoelectrics is critical for optimizing the performance of flexible and wearable ultrasound transducers, such as screen-printed PVDF devices. Standard charge measurement techniques, l
Externí odkaz:
http://arxiv.org/abs/2410.16581
This work explores the feasibility of employing ultrasound (US) US technology in a wrist-worn IoT device for low-power, high-fidelity heart-rate (HR) extraction. US offers deep tissue penetration and can monitor pulsatile arterial blood flow in large
Externí odkaz:
http://arxiv.org/abs/2410.16219
Autor:
Ottaviano, Alessandro, Balas, Robert, Fischer, Tim, Benz, Thomas, Bartolini, Andrea, Benini, Luca
The increasing complexity of real-time control algorithms and the trend toward 2.5D technology necessitate the development of scalable controllers for managing the complex, integrated operation of chiplets within 2.5D systems-in-package. These contro
Externí odkaz:
http://arxiv.org/abs/2410.15985
Autor:
Hamdi, Mohamed Amine, Daghero, Francesco, Sarda, Giuseppe Maria, Van Delm, Josse, Symons, Arne, Benini, Luca, Verhelst, Marian, Pagliari, Daniele Jahier, Burrello, Alessio
Streamlining the deployment of Deep Neural Networks (DNNs) on heterogeneous edge platforms, coupling within the same micro-controller unit (MCU) instruction processors and hardware accelerators for tensor computations, is becoming one of the crucial
Externí odkaz:
http://arxiv.org/abs/2410.08855
Autor:
Zelioli, Enrico, Ottaviano, Alessandro, Balas, Robert, Wistoff, Nils, Garofalo, Angelo, Benini, Luca
The widespread diffusion of compute-intensive edge-AI workloads and the stringent demands of modern autonomous systems require advanced heterogeneous embedded architectures. Such architectures must support high-performance and reliable execution of p
Externí odkaz:
http://arxiv.org/abs/2410.07798
This study investigates the application and performance of the Segment Anything Model 2 (SAM2) in the challenging task of video camouflaged object segmentation (VCOS). VCOS involves detecting objects that blend seamlessly in the surroundings for vide
Externí odkaz:
http://arxiv.org/abs/2409.18653
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
The new generation of domain-specific AI accelerators is characterized by rapidly increasing demands for bulk data transfers, as opposed to small, latency-critical cache line transfers typical of traditional cache-coherent systems. In this paper, we
Externí odkaz:
http://arxiv.org/abs/2409.17606
Autor:
Mei, Lan, Ingolfsson, Thorir Mar, Cioflan, Cristian, Kartsch, Victor, Cossettini, Andrea, Wang, Xiaying, Benini, Luca
Driven by the progress in efficient embedded processing, there is an accelerating trend toward running machine learning models directly on wearable Brain-Machine Interfaces (BMIs) to improve portability and privacy and maximize battery life. However,
Externí odkaz:
http://arxiv.org/abs/2409.10654