Zobrazeno 1 - 10
of 358
pro vyhledávání: '"Axel Jantsch"'
Publikováno v:
IEEE Access, Vol 12, Pp 109847-109860 (2024)
Today, there exists a large number of different embedded hardware platforms for accelerating the inference of Deep Neural Networks (DNNs). To enable rapid application development, a number of prediction frameworks have been proposed to estimate the D
Externí odkaz:
https://doaj.org/article/932c1e0df8954c7c99e4b0f2d7845356
Autor:
Muhammad Noman Sohail, Adeel Anjum, Iftikhar Ahmed Saeed, Madiha Haider Syed, Axel Jantsch, Semeen Rehman
Publikováno v:
IEEE Access, Vol 12, Pp 51176-51192 (2024)
Connecting smart industrial components to computer networks revolutionizes business operations. However, in the Industrial Internet of Things (IIoT), the sharing of data has bandwidth, computational, and privacy issues. Researchers presented cloud co
Externí odkaz:
https://doaj.org/article/6aae9e0f72fd402a9f99afe9a55c9bf3
Publikováno v:
IEEE Access, Vol 10, Pp 108194-108204 (2022)
The importance of anomaly detection in multivariate time series has led to the development of several prominent deep learning solutions. As a part of the anomaly detection process, the scoring method has shown to be of significant importance when sep
Externí odkaz:
https://doaj.org/article/fa60373f1c2b40059b6636209392f47f
Publikováno v:
IEEE Access, Vol 9, Pp 65078-65090 (2021)
The proliferation of smart sensor nodes for IoT deployments comes with requirements of energy efficiency and to fulfil functional requirements, but it also demands a fast time to market. As a result, we need to facilitate the design of these IoT node
Externí odkaz:
https://doaj.org/article/f86a04ace02e45e8adf5e2dd789d3872
Publikováno v:
IEEE Access, Vol 9, Pp 3545-3556 (2021)
With new accelerator hardware for Deep Neural Networks (DNNs), the computing power for Artificial Intelligence (AI) applications has increased rapidly. However, as DNN algorithms become more complex and optimized for specific applications, latency re
Externí odkaz:
https://doaj.org/article/1546e71ebabf4c1381e2e6170570a848
Autor:
Martin Lechner, Axel Jantsch
Publikováno v:
IEEE Access, Vol 9, Pp 110074-110084 (2021)
With more powerful yet efficient embedded devices and accelerators being available for Deep Neural Networks (DNN), machine learning is becoming an integral part of edge computing. As the number of such devices increases, finding the best platform for
Externí odkaz:
https://doaj.org/article/df5e7bce502d4486b9566620b01c45b2
Autor:
Maximilian Gotzinger, David Juhasz, Nima Taherinejad, Edwin Willegger, Benedikt Tutzer, Pasi Liljeberg, Axel Jantsch, Amir M. Rahmani
Publikováno v:
IEEE Access, Vol 8, Pp 141373-141394 (2020)
The role of smart and autonomous systems is becoming vital in many areas of industry and society. Expectations from such systems continuously rise and become more ambitious: long lifetime, high reliability, high performance, energy efficiency, and ad
Externí odkaz:
https://doaj.org/article/b029bad1dab745be95cad45d1aad77ea
Publikováno v:
Electronic Proceedings in Theoretical Computer Science, Vol 247, Iss Proc. SNR 2017, Pp 1-17 (2017)
Cyber-Physical Systems (CPS) pose new challenges to verification and validation that go beyond the proof of functional correctness based on high-level models. Particular challenges are, in particular for formal methods, its heterogeneity and scalabil
Externí odkaz:
https://doaj.org/article/7df92b6c7a6e453a9e5791eef9b8c01a
Publikováno v:
Annals of computer science and information systems, Vol 9, Pp 117-124 (2016)
Externí odkaz:
https://doaj.org/article/e3f9c464a78d41a0a8255bfe2ebfd4c5
Publikováno v:
Journal of Electrical and Computer Engineering, Vol 2015 (2015)
On-chip computing platforms are evolving from single-core bus-based systems to many-core network-based systems, which are referred to as On-chip Large-scale Parallel Computing Architectures (OLPCs) in the paper. Homogenous OLPCs feature strong regula
Externí odkaz:
https://doaj.org/article/b3bde0ac838049a3bb9ee2dced644479