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pro vyhledávání: '"Jung, Victor"'
Autor:
Scherer, Moritz, Macan, Luka, Jung, Victor, Wiese, Philip, Bompani, Luca, Burrello, Alessio, Conti, Francesco, Benini, Luca
With the rise of Embodied Foundation Models (EFMs), most notably Small Language Models (SLMs), adapting Transformers for edge applications has become a very active field of research. However, achieving end-to-end deployment of SLMs on microcontroller
Externí odkaz:
http://arxiv.org/abs/2408.04413
Autor:
Wiese, Philip, İslamoğlu, Gamze, Scherer, Moritz, Macan, Luka, Jung, Victor J. B., Burrello, Alessio, Conti, Francesco, Benini, Luca
One of the challenges for Tiny Machine Learning (tinyML) is keeping up with the evolution of Machine Learning models from Convolutional Neural Networks to Transformers. We address this by leveraging a heterogeneous architectural template coupling RIS
Externí odkaz:
http://arxiv.org/abs/2408.02473
The impact of transformer networks is booming, yet, they come with significant computational complexity. It is therefore essential to understand how to optimally map and execute these networks on modern neural processor hardware. So far, literature o
Externí odkaz:
http://arxiv.org/abs/2406.09804
Transformer networks are rapidly becoming SotA in many fields, such as NLP and CV. Similarly to CNN, there is a strong push for deploying Transformer models at the extreme edge, ultimately fitting the tiny power budget and memory footprint of MCUs. H
Externí odkaz:
http://arxiv.org/abs/2404.02945
Autor:
Busia, Paola, Scrugli, Matteo Antonio, Jung, Victor Jean-Baptiste, Benini, Luca, Meloni, Paolo
Wearable systems for the continuous and real-time monitoring of cardiovascular diseases are becoming widespread and valuable assets in diagnosis and therapy. A promising approach for real-time analysis of the electrocardiographic (ECG) signal and the
Externí odkaz:
http://arxiv.org/abs/2402.10748
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
To meet the growing need for computational power for DNNs, multiple specialized hardware architectures have been proposed. Each DNN layer should be mapped onto the hardware with the most efficient schedule, however, SotA schedulers struggle to consis
Externí odkaz:
http://arxiv.org/abs/2304.12931
Akademický článek
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Publikováno v:
Multichannel News. 1/29/2007, Vol. 28 Issue 5, p34-34. 1/2p.
