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pro vyhledávání: '"Bernardo, Paul Palomero"'
Autor:
Lübeck, Konstantin, Jung, Alexander Louis-Ferdinand, Wedlich, Felix, Müller, Mika Markus, Peccia, Federico Nicolás, Thömmes, Felix, Steinmetz, Jannik, Biermaier, Valentin, Frischknecht, Adrian, Bernardo, Paul Palomero, Bringmann, Oliver
Implementing Deep Neural Networks (DNNs) on resource-constrained edge devices is a challenging task that requires tailored hardware accelerator architectures and a clear understanding of their performance characteristics when executing the intended A
Externí odkaz:
http://arxiv.org/abs/2409.08595
Epilepsy is the most common, chronic, neurological disease worldwide and is typically accompanied by reoccurring seizures. Neuro implants can be used for effective treatment by suppressing an upcoming seizure upon detection. Due to the restricted siz
Externí odkaz:
http://arxiv.org/abs/2406.16948
As machine learning applications continue to evolve, the demand for efficient hardware accelerators, specifically tailored for deep neural networks (DNNs), becomes increasingly vital. In this paper, we propose a configurable memory hierarchy framewor
Externí odkaz:
http://arxiv.org/abs/2404.15823
Hardware Accelerator and Neural Network Co-Optimization for Ultra-Low-Power Audio Processing Devices
Autor:
Gerum, Christoph, Frischknecht, Adrian, Hald, Tobias, Bernardo, Paul Palomero, Lübeck, Konstantin, Bringmann, Oliver
The increasing spread of artificial neural networks does not stop at ultralow-power edge devices. However, these very often have high computational demand and require specialized hardware accelerators to ensure the design meets power and performance
Externí odkaz:
http://arxiv.org/abs/2209.03807
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