Zobrazeno 1 - 10
of 48 330
pro vyhledávání: '"Edge Devices"'
The growing necessity for enhanced processing capabilities in edge devices with limited resources has led us to develop effective methods for improving high-performance computing (HPC) applications. In this paper, we introduce LASP (Lightweight Autot
Externí odkaz:
http://arxiv.org/abs/2501.01057
Autor:
Yeom, Seul-Ki, Kim, Tae-Ho
Transformer-based architectures have demonstrated remarkable success across various domains, but their deployment on edge devices remains challenging due to high memory and computational demands. In this paper, we introduce a novel Reuse Attention me
Externí odkaz:
http://arxiv.org/abs/2412.02344
This paper addresses the challenges of Online Action Recognition (OAR), a framework that involves instantaneous analysis and classification of behaviors in video streams. OAR must operate under stringent latency constraints, making it an indispensabl
Externí odkaz:
http://arxiv.org/abs/2412.01267
Autor:
Wu, Meihan, Chang, Tao, Miao, Cui, Zhou, Jie, Li, Chun, Xu, Xiangyu, Li, Ming, Wang, Xiaodong
Federated learning research has recently shifted from Convolutional Neural Networks (CNNs) to Vision Transformers (ViTs) due to their superior capacity. ViTs training demands higher computational resources due to the lack of 2D inductive biases inher
Externí odkaz:
http://arxiv.org/abs/2412.00334
Real-time object localization on edge devices is fundamental for numerous applications, ranging from surveillance to industrial automation. Traditional frameworks, such as object detection, segmentation, and keypoint detection, struggle in resource-c
Externí odkaz:
http://arxiv.org/abs/2411.15653
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
In this paper, we present an experimental comparison of various graph-based approximate nearest neighbor (ANN) search algorithms deployed on edge devices for real-time nearest neighbor search applications, such as smart city infrastructure and autono
Externí odkaz:
http://arxiv.org/abs/2411.14006
Recent advancements in machine learning, particularly through deep learning architectures like PointNet, have transformed the processing of three-dimensional (3D) point clouds, significantly improving 3D object classification and segmentation tasks.
Externí odkaz:
http://arxiv.org/abs/2411.14585
Deploying Retrieval Augmented Generation (RAG) on resource-constrained edge devices is challenging due to limited memory and processing power. In this work, we propose EdgeRAG which addresses the memory constraint by pruning embeddings within cluster
Externí odkaz:
http://arxiv.org/abs/2412.21023
This paper proposes small and efficient machine learning models (TinyML) for resource-constrained edge devices, specifically for on-device indoor localisation. Typical approaches for indoor localisation rely on centralised remote processing of data t
Externí odkaz:
http://arxiv.org/abs/2412.09289