S Liu, W Zhou, Z Zhou, B Guo, M Wang… - Proceedings of the …, 2024 - dl.acm.org
There is a growing demand to deploy computation-intensive deep learning (DL) models on resource-constrained mobile devices for real-time intelligent applications. Equipped with a …
C Xu, X Hou, J Liu, C Li, T Huang, X Zhu… - 2023 IEEE …, 2023 - ieeexplore.ieee.org
The explosive growth of various types of big data and advances in AI technologies have catalyzed a new type of workloads called multi-modal DNNs. Multi-modal DNNs are capable …
End-to-end edge intelligence systems (EIS) at the Internet's edge have been receiving ever- increasing attention in both academia and industry, driven by a myriad of factors, including …
Split Neural Network is a state-of-the-art distributed machine learning technique to enable on-device deep learning applications without accessing to local data. Recently, Abuadbba …
Recent years witness a trend of applying large-scale distributed deep learning algorithms (HPC AI) in both business and scientific computing areas, whose goal is to speed up the …
In the semiconductor industry, the utilization of advanced visual inspection technology confronts challenges due to the scarcity of the defect data and the substantial sizes of the …
Neural networks (NNs) are becoming an increasingly important part of mobile robot control systems. Compared with traditional methods, NNs (and other data-driven techniques) …
Atrial Fibrillation (AF) is the most common cardiac rhythm disorder. Advance knowledge of an imminent switch from sinus rhythm (SR) to AF could prompt patients to take preventive …
Today, deep learning optimization is primarily driven by research focused on achieving high inference accuracy and reducing latency. However, the energy efficiency aspect is often …