Zobrazeno 1 - 10
of 102
pro vyhledávání: '"FRÖNING, HOLGER"'
Autor:
Barley, Daniel, Fröning, Holger
The ever-growing scale of deep neural networks (DNNs) has lead to an equally rapid growth in computational resource requirements. Many recent architectures, most prominently Large Language Models, have to be trained using supercomputers with thousand
Externí odkaz:
http://arxiv.org/abs/2409.11902
Publikováno v:
Proceedings of the 38th ACM International Conference on Supercomputing (ICS '24), June 4--7, 2024, Kyoto, Japan
Anomaly detection in distributed systems such as High-Performance Computing (HPC) clusters is vital for early fault detection, performance optimisation, security monitoring, reliability in general but also operational insights. Deep Neural Networks h
Externí odkaz:
http://arxiv.org/abs/2405.07749
Efficiently finding subgraph embeddings in large graphs is crucial for many application areas like biology and social network analysis. Set intersections are the predominant and most challenging aspect of current join-based subgraph query processing
Externí odkaz:
http://arxiv.org/abs/2402.17559
Autor:
Brückerhoff-Plückelmann, Frank, Borras, Hendrik, Klein, Bernhard, Varri, Akhil, Becker, Marlon, Dijkstra, Jelle, Brückerhoff, Martin, Wright, C. David, Salinga, Martin, Bhaskaran, Harish, Risse, Benjamin, Fröning, Holger, Pernice, Wolfram
Biological neural networks effortlessly tackle complex computational problems and excel at predicting outcomes from noisy, incomplete data, a task that poses significant challenges to traditional processors. Artificial neural networks (ANNs), inspire
Externí odkaz:
http://arxiv.org/abs/2401.17915
Resistive memory is a promising alternative to SRAM, but is also an inherently unstable device that requires substantial effort to ensure correct read and write operations. To avoid the associated costs in terms of area, time and energy, the present
Externí odkaz:
http://arxiv.org/abs/2401.05820
In recent decades, High Performance Computing (HPC) has undergone significant enhancements, particularly in the realm of hardware platforms, aimed at delivering increased processing power while keeping power consumption within reasonable limits. The
Externí odkaz:
http://arxiv.org/abs/2310.00256
The energy efficiency of analog forms of computing makes it one of the most promising candidates to deploy resource-hungry machine learning tasks on resource-constrained system such as mobile or embedded devices. However, it is well known that for an
Externí odkaz:
http://arxiv.org/abs/2309.14292
High Performance Computing (HPC) benefits from different improvements during last decades, specially in terms of hardware platforms to provide more processing power while maintaining the power consumption at a reasonable level. The Intelligence Proce
Externí odkaz:
http://arxiv.org/abs/2309.08946
Deep neural networks are extremely successful in various applications, however they exhibit high computational demands and energy consumption. This is exacerbated by stuttering technology scaling, prompting the need for novel approaches to handle inc
Externí odkaz:
http://arxiv.org/abs/2212.10430
Compressing neural network architectures is important to allow the deployment of models to embedded or mobile devices, and pruning and quantization are the major approaches to compress neural networks nowadays. Both methods benefit when compression p
Externí odkaz:
http://arxiv.org/abs/2212.07818