Zobrazeno 1 - 10
of 3 234
pro vyhledávání: '"Stamoulis, A."'
Modern edge data centers simultaneously handle multiple Deep Neural Networks (DNNs), leading to significant challenges in workload management. Thus, current management systems must leverage the architectural heterogeneity of new embedded systems to e
Externí odkaz:
http://arxiv.org/abs/2411.17867
Autor:
Paramanayakam, Varatheepan, Karatzas, Andreas, Anagnostopoulos, Iraklis, Stamoulis, Dimitrios
The advanced function-calling capabilities of foundation models open up new possibilities for deploying agents to perform complex API tasks. However, managing large amounts of data and interacting with numerous APIs makes function calling hardware-in
Externí odkaz:
http://arxiv.org/abs/2411.15399
Autor:
Hatzel, Meike, Kreutzer, Stephan, Protopapas, Evangelos, Reich, Florian, Stamoulis, Giannos, Wiederrecht, Sebastian
We prove that there exist four operations such that given any two strongly $2$-connected digraphs $H$ and $D$ where $H$ is a butterfly-minor of $D$, there exists a sequence $D_0,\dots, D_n$ where $D_0=H$, $D_n=D$ and for every $0\leq i\leq n-1$, $D_i
Externí odkaz:
http://arxiv.org/abs/2411.09791
Model order reduction (MOR) is essential in integrated circuit design, particularly when dealing with large-scale electromagnetic models extracted from complex designs. The numerous passive elements introduced in these models pose significant challen
Externí odkaz:
http://arxiv.org/abs/2411.13571
Given a graph $G$ and a vertex set $X$, the annotated treewidth tw$(G,X)$ of $X$ in $G$ is the maximum treewidth of an $X$-rooted minor of $G$, i.e., a minor $H$ where the model of each vertex of $H$ contains some vertex of $X$. That way, tw$(G,X)$ c
Externí odkaz:
http://arxiv.org/abs/2406.18465
Autor:
Singh, Simranjit, Fore, Michael, Karatzas, Andreas, Lee, Chaehong, Jian, Yanan, Shangguan, Longfei, Yu, Fuxun, Anagnostopoulos, Iraklis, Stamoulis, Dimitrios
As Large Language Models (LLMs) broaden their capabilities to manage thousands of API calls, they are confronted with complex data operations across vast datasets with significant overhead to the underlying system. In this work, we introduce LLM-dCac
Externí odkaz:
http://arxiv.org/abs/2406.06799
As interest in "reformulating" the 3D Visual Question Answering (VQA) problem in the context of foundation models grows, it is imperative to assess how these new paradigms influence existing closed-vocabulary datasets. In this case study, we evaluate
Externí odkaz:
http://arxiv.org/abs/2405.18831
Autor:
Fore, Michael, Singh, Simranjit, Lee, Chaehong, Pandey, Amritanshu, Anastasopoulos, Antonios, Stamoulis, Dimitrios
Misinformation regarding climate change is a key roadblock in addressing one of the most serious threats to humanity. This paper investigates factual accuracy in large language models (LLMs) regarding climate information. Using true/false labeled Q&A
Externí odkaz:
http://arxiv.org/abs/2405.19563
Autor:
Singh, Simranjit, Karatzas, Andreas, Fore, Michael, Anagnostopoulos, Iraklis, Stamoulis, Dimitrios
State-of-the-art sequential reasoning in Large Language Models (LLMs) has expanded the capabilities of Copilots beyond conversational tasks to complex function calling, managing thousands of API calls. However, the tendency of compositional prompting
Externí odkaz:
http://arxiv.org/abs/2405.17438
In this preliminary study, we investigate a GPT-driven intent-based reasoning approach to streamline tool selection for large language models (LLMs) aimed at system efficiency. By identifying the intent behind user prompts at runtime, we narrow down
Externí odkaz:
http://arxiv.org/abs/2404.15804