Zobrazeno 1 - 10
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pro vyhledávání: '"Gerstlauer, A."'
The training of deep and/or convolutional neural networks (DNNs/CNNs) is traditionally done on servers with powerful CPUs and GPUs. Recent efforts have emerged to localize machine learning tasks fully on the edge. This brings advantages in reduced la
Externí odkaz:
http://arxiv.org/abs/2409.09083
In the era of deep learning (DL), convolutional neural networks (CNNs), and large language models (LLMs), machine learning (ML) models are becoming increasingly complex, demanding significant computational resources for both inference and training st
Externí odkaz:
http://arxiv.org/abs/2405.15079
FPGAs are a promising platform for accelerating Deep Learning (DL) applications, due to their high performance, low power consumption, and reconfigurability. Recently, the leading FPGA vendors have enhanced their architectures to more efficiently sup
Externí odkaz:
http://arxiv.org/abs/2404.11066
Autor:
Alcorta, Erika S., Madhav, Mahesh, Tetrick, Scott, Yadwadkar, Neeraja J., Gerstlauer, Andreas
Modern computer designs support composite prefetching, where multiple individual prefetcher components are used to target different memory access patterns. However, multiple prefetchers competing for resources can drastically hurt performance, especi
Externí odkaz:
http://arxiv.org/abs/2307.08635
Autor:
Farley, Jackson, Gerstlauer, Andreas
Publikováno v:
Designing Modern Embedded Systems: Software, Hardware, and Applications, Proceedings of the 7th IFIP TC 10 International Embedded Systems Symposium (IESS 2022), Springer, 2023
A rising research challenge is running costly machine learning (ML) networks locally on resource-constrained edge devices. ML networks with large convolutional layers can easily exceed available memory, increasing latency due to excessive OS swapping
Externí odkaz:
http://arxiv.org/abs/2107.06960
Cross-core communication is increasingly a bottleneck as the number of processing elements increase per system-on-chip. Typical hardware solutions to cross-core communication are often inflexible; while software solutions are flexible, they have perf
Externí odkaz:
http://arxiv.org/abs/2012.05181
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Autor:
Stanley-Marbell, Phillip, Alaghi, Armin, Carbin, Michael, Darulova, Eva, Dolecek, Lara, Gerstlauer, Andreas, Gillani, Ghayoor, Jevdjic, Djordje, Moreau, Thierry, Cacciotti, Mattia, Daglis, Alexandros, Jerger, Natalie Enright, Falsafi, Babak, Misailovic, Sasa, Sampson, Adrian, Zufferey, Damien
When a computational task tolerates a relaxation of its specification or when an algorithm tolerates the effects of noise in its execution, hardware, programming languages, and system software can trade deviations from correct behavior for lower reso
Externí odkaz:
http://arxiv.org/abs/1809.05859