Zobrazeno 1 - 10
of 18 327
pro vyhledávání: '"Ultra-low latency"'
Autor:
Su, Weigao, Shrivastav, Vishal
Achieving low remote memory access latency remains the primary challenge in realizing memory disaggregation over Ethernet within the datacenters. We present EDM that attempts to overcome this challenge using two key ideas. First, while existing netwo
Externí odkaz:
http://arxiv.org/abs/2411.08300
Autor:
Cheng, Longbiao, Pandey, Ashutosh, Xu, Buye, Delbruck, Tobi, Ithapu, Vamsi Krishna, Liu, Shih-Chii
Deep learning-based speech enhancement (SE) methods often face significant computational challenges when needing to meet low-latency requirements because of the increased number of frames to be processed. This paper introduces the SlowFast framework
Externí odkaz:
http://arxiv.org/abs/2411.02019
The 6G mobile networks will feature the widespread deployment of AI algorithms at the network edge, which provides a platform for supporting robotic edge intelligence systems. In such a system, a large-scale knowledge graph (KG) is operated at an edg
Externí odkaz:
http://arxiv.org/abs/2409.13319
There is a broad consensus that artificial intelligence (AI) will be a defining component of the sixth-generation (6G) networks. As a specific instance, AI-empowered sensing will gather and process environmental perception data at the network edge, g
Externí odkaz:
http://arxiv.org/abs/2407.13360
Autor:
Li, Jiahao1 (AUTHOR) 3220215118@bit.edu.cn, Xu, Ming1 (AUTHOR), Chen, He1 (AUTHOR) chenhe@bit.edu.cn, Liu, Wenchao1 (AUTHOR), Chen, Liang1 (AUTHOR), Xie, Yizhuang1 (AUTHOR)
Publikováno v:
Remote Sensing. Sep2024, Vol. 16 Issue 17, p3200. 26p.
Autor:
Borella, Lorenzo, Coppi, Alberto, Pazzini, Jacopo, Stanco, Andrea, Trenti, Marco, Triossi, Andrea, Zanetti, Marco
Tensor Networks (TNs) are a computational paradigm used for representing quantum many-body systems. Recent works have shown how TNs can also be applied to perform Machine Learning (ML) tasks, yielding comparable results to standard supervised learnin
Externí odkaz:
http://arxiv.org/abs/2409.16075
Autor:
Wu, Haibin, Braun, Sebastian
Speech enhancement models should meet very low latency requirements typically smaller than 5 ms for hearing assistive devices. While various low-latency techniques have been proposed, comparing these methods in a controlled setup using DNNs remains b
Externí odkaz:
http://arxiv.org/abs/2409.10358
Autor:
Mohammed, Saja Majeed1 (AUTHOR), Al-Barrak, Alyaa2 (AUTHOR), Mahmood, Noof T.3 (AUTHOR) noof.t@albayan.edu.iq
Publikováno v:
Ingénierie des Systèmes d'Information. Jun2024, Vol. 29 Issue 3, p1195-1208. 14p.
Publikováno v:
2023 International Conference on Field Programmable Technology (ICFPT), Yokohama, Japan, 2023, pp. 60-68
Field-programmable gate arrays (FPGAs) are widely used to implement deep learning inference. Standard deep neural network inference involves the computation of interleaved linear maps and nonlinear activation functions. Prior work for ultra-low laten
Externí odkaz:
http://arxiv.org/abs/2309.02334
Autor:
Westhausen, Nils L., Meyer, Bernd T.
Speech enhancement in hearing aids is a challenging task since the hardware limits the number of possible operations and the latency needs to be in the range of only a few milliseconds. We propose a deep-learning model compatible with these limitatio
Externí odkaz:
http://arxiv.org/abs/2307.08858