Zobrazeno 1 - 10
of 20
pro vyhledávání: '"Ottavi, Gianmarco"'
Autor:
Tedeschi, Riccardo, Valente, Luca, Ottavi, Gianmarco, Zelioli, Enrico, Wistoff, Nils, Giacometti, Massimiliano, Sajjad, Abdul Basit, Benini, Luca, Rossi, Davide
Symmetric Multi-Processing (SMP) based on cache coherency is crucial for high-end embedded systems like automotive applications. RISC-V is gaining traction, and open-source hardware (OSH) platforms offer solutions to issues such as IP costs and vendo
Externí odkaz:
http://arxiv.org/abs/2407.19895
Autor:
Paulin, Gianna, Scheffler, Paul, Benz, Thomas, Cavalcante, Matheus, Fischer, Tim, Eggimann, Manuel, Zhang, Yichao, Wistoff, Nils, Bertaccini, Luca, Colagrande, Luca, Ottavi, Gianmarco, Gürkaynak, Frank K., Rossi, Davide, Benini, Luca
We present Occamy, a 432-core RISC-V dual-chiplet 2.5D system for efficient sparse linear algebra and stencil computations on FP64 and narrow (32-, 16-, 8-bit) SIMD FP data. Occamy features 48 clusters of RISC-V cores with custom extensions, two 64-b
Externí odkaz:
http://arxiv.org/abs/2406.15068
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:
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
Autor:
Garofalo, Angelo, Ottavi, Gianmarco, Conti, Francesco, Karunaratne, Geethan, Boybat, Irem, Benini, Luca, Rossi, Davide
Deployment of modern TinyML tasks on small battery-constrained IoT devices requires high computational energy efficiency. Analog In-Memory Computing (IMC) using non-volatile memory (NVM) promises major efficiency improvements in deep neural network (
Externí odkaz:
http://arxiv.org/abs/2201.01089
Autor:
Ottavi, Gianmarco, Karunaratne, Geethan, Conti, Francesco, Boybat, Irem, Benini, Luca, Rossi, Davide
Publikováno v:
2021 IEEE 3rd International Conference on Artificial Intelligence Circuits and Systems (AICAS)
In-Memory Acceleration (IMA) promises major efficiency improvements in deep neural network (DNN) inference, but challenges remain in the integration of IMA within a digital system. We propose a heterogeneous architecture coupling 8 RISC-V cores with
Externí odkaz:
http://arxiv.org/abs/2109.01404
Autor:
Ottavi, Gianmarco, Garofalo, Angelo, Tagliavini, Giuseppe, Conti, Francesco, Benini, Luca, Rossi, Davide
Low bit-width Quantized Neural Networks (QNNs) enable deployment of complex machine learning models on constrained devices such as microcontrollers (MCUs) by reducing their memory footprint. Fine-grained asymmetric quantization (i.e., different bit-w
Externí odkaz:
http://arxiv.org/abs/2010.04073
Autor:
Montagna, Fabio, Mach, Stefan, Benatti, Simone, Garofalo, Angelo, Ottavi, Gianmarco, Benini, Luca, Rossi, Davide, Tagliavini, Giuseppe
Recent applications in the domain of near-sensor computing require the adoption of floating-point arithmetic to reconcile high precision results with a wide dynamic range. In this paper, we propose a multi-core computing cluster that leverages the fi
Externí odkaz:
http://arxiv.org/abs/2008.12243
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.