Zobrazeno 1 - 10
of 98 397
pro vyhledávání: '"Memory usage"'
Parameter-efficient fine-tuning (PEFT) is an effective method for adapting pre-trained vision models to downstream tasks by tuning a small subset of parameters. Among PEFT methods, sparse tuning achieves superior performance by only adjusting the wei
Externí odkaz:
http://arxiv.org/abs/2411.01800
Efficient deployment of neural networks on resource-constrained hardware demands optimal use of on-chip memory. In event-based processors, this is particularly critical for routing architectures, where substantial memory is dedicated to managing netw
Externí odkaz:
http://arxiv.org/abs/2412.01575
Autor:
Abbasi, Reza, Lim, Sernam
The rapid growth in machine learning models, especially in natural language processing and computer vision, has led to challenges when running these models on hardware with limited resources. This paper introduces Superpipeline, a new framework desig
Externí odkaz:
http://arxiv.org/abs/2410.08791
Autor:
Gonzalez-Gomez, Jeferson, Ibarra-Campos, Jose Alejandro, Sandoval-Morales, Jesus Yamir, Bauer, Lars, Henkel, Jörg
Covert channel attacks have been continuously studied as severe threats to modern computing systems. Software-based covert channels are a typically hard-to-detect branch of these attacks, since they leverage virtual resources to establish illegitimat
Externí odkaz:
http://arxiv.org/abs/2409.13310
Scientific workflow management systems enable the reproducible execution of data analysis pipelines on cluster infrastructures managed by resource managers such as Kubernetes, Slurm, or HTCondor. These resource managers require resource estimates for
Externí odkaz:
http://arxiv.org/abs/2408.12290
Sensitivity analysis of fractional linear systems based on random walks with negligible memory usage
A random walk-based method is proposed to efficiently compute the solution of a large class of fractional in time linear systems of differential equations (linear F-ODE systems), along with the derivatives with respect to the system parameters. Such
Externí odkaz:
http://arxiv.org/abs/2408.04351
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
Publikováno v:
Industrial Networks and Intelligent Systems,2023
Internet of Everything (IoE) is a newly emerging trend, especially in homes. Marketing forces toward smart homes are also accelerating the spread of IoE devices in households. An obvious risk of the rapid adoption of these smart devices is that many
Externí odkaz:
http://arxiv.org/abs/2404.19480
Autor:
Kim, Taeho, Wang, Yanming, Chaturvedi, Vatshank, Gupta, Lokesh, Kim, Seyeon, Kwon, Yongin, Ha, Sangtae
Fine-tuning pre-trained large language models (LLMs) with limited hardware presents challenges due to GPU memory constraints. Various distributed fine-tuning methods have been proposed to alleviate memory constraints on GPU. However, determining the
Externí odkaz:
http://arxiv.org/abs/2404.10933
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.