Optimising resource management for embedded machine learning
Machine learning inference is increasingly being executed locally on mobile and embedded
platforms, due to the clear advantages in latency, privacy and connectivity. In this paper, we …
platforms, due to the clear advantages in latency, privacy and connectivity. In this paper, we …
Optimising resource management for embedded machine learning
L Xun, L Tran-Thanh, BM Al-Hashimi… - Proceedings of the 23rd …, 2020 - dl.acm.org
Machine learning inference is increasingly being executed locally on mobile and embedded
platforms, due to the clear advantages in latency, privacy and connectivity. In this paper, we …
platforms, due to the clear advantages in latency, privacy and connectivity. In this paper, we …
Optimising Resource Management for Embedded Machine Learning
L Xun, L Tran-Thanh, BM Al-Hashimi… - arXiv preprint arXiv …, 2021 - arxiv.org
Machine learning inference is increasingly being executed locally on mobile and embedded
platforms, due to the clear advantages in latency, privacy and connectivity. In this paper, we …
platforms, due to the clear advantages in latency, privacy and connectivity. In this paper, we …
Optimising Resource Management for Embedded Machine Learning
L Xun, L Tran-Thanh, BM Al-Hashimi… - arXiv e …, 2021 - ui.adsabs.harvard.edu
Abstract Machine learning inference is increasingly being executed locally on mobile and
embedded platforms, due to the clear advantages in latency, privacy and connectivity. In this …
embedded platforms, due to the clear advantages in latency, privacy and connectivity. In this …
[PDF][PDF] Optimising Resource Management for Embedded Machine Learning
L Xun, L Tran-Thanh, BM Al-Hashimi, GV Merrett - scholar.archive.org
Machine learning inference is increasingly being executed locally on mobile and embedded
platforms, due to the clear advantages in latency, privacy and connectivity. In this paper, we …
platforms, due to the clear advantages in latency, privacy and connectivity. In this paper, we …
[PDF][PDF] Optimising Resource Management for Embedded Machine Learning
L Xun, L Tran-Thanh, BM Al-Hashimi, GV Merrett - past.date-conference.com
Machine learning inference is increasingly being executed locally on mobile and embedded
platforms, due to the clear advantages in latency, privacy and connectivity. In this paper, we …
platforms, due to the clear advantages in latency, privacy and connectivity. In this paper, we …
Optimising resource management for embedded machine learning
L Xun, L Tran-Thanh, B Al-Hashimi, G Merrett - 2020 - eprints.soton.ac.uk
Machine learning inference is increasingly being executed locally on mobile and embedded
platforms, due to the clear advantages in latency, privacy and connectivity. In this paper, we …
platforms, due to the clear advantages in latency, privacy and connectivity. In this paper, we …