Poster: Scaling up deep neural network optimization for edge inference
Deep neural networks (DNNs) have been increasingly deployed on and integrated with
edge devices, such as mobile phones, drones, robots and wearables. Compared to cloud …
edge devices, such as mobile phones, drones, robots and wearables. Compared to cloud …
Deep reinforcement learning for edge computing and resource allocation in 5G beyond
Y Dai, D Xu, K Zhang, Y Lu… - 2019 IEEE 19th …, 2019 - ieeexplore.ieee.org
By extending computation capacity to the edge of wireless networks, edge computing has
the potential to enable computation-intensive and delay-sensitive applications in 5G and …
the potential to enable computation-intensive and delay-sensitive applications in 5G and …
Dynamic DNN decomposition for lossless synergistic inference
B Zhang, T Xiang, H Zhang, T Li… - 2021 IEEE 41st …, 2021 - ieeexplore.ieee.org
Deep neural networks (DNNs) sustain high performance in today's data processing
applications. DNN inference is resource-intensive thus is difficult to fit into a mobile device …
applications. DNN inference is resource-intensive thus is difficult to fit into a mobile device …
Analyse or transmit: Utilising correlation at the edge with deep reinforcement learning
Millions of sensors, cameras, meters, and other edge devices are deployed in networks to
collect and analyse data. In many cases, such devices are powered only by Energy …
collect and analyse data. In many cases, such devices are powered only by Energy …
EEAI: An End-edge Architecture for Accelerating Deep Neural Network Inference
Deep Neural Networks (DNNs), as a key technology for Artificial Intelligence (AI)
applications in the 5G era, have been widely used in the field of mobile intelligence …
applications in the 5G era, have been widely used in the field of mobile intelligence …
Dynamic Split Computing-Aware Mixed-Precision Quantization for Efficient Deep Edge Intelligence
N Nagamatsu, Y Hara-Azumi - … on Trust, Security and Privacy in …, 2023 - ieeexplore.ieee.org
Deploying large deep neural networks (DNNs) on IoT and mobile devices poses a
significant challenge due to hardware resource limitations. To address this challenge, an …
significant challenge due to hardware resource limitations. To address this challenge, an …
Multi-agent driven resource allocation and interference management for deep edge networks
Sixth generation mobile networks (6G) may experience a huge evolution on vertical industry
scenarios, where deep edge networks () become an important network structure for the …
scenarios, where deep edge networks () become an important network structure for the …
Optimum splitting computing for DNN training through next generation smart networks: a multi-tier deep reinforcement learning approach
Deep neural networks (DNNs) involving massive neural nodes grouped into different neural
layers have been a promising innovation for function approximation and inference, which …
layers have been a promising innovation for function approximation and inference, which …
Edge artificial intelligence for 6G: Vision, enabling technologies, and applications
KB Letaief, Y Shi, J Lu, J Lu - IEEE Journal on Selected Areas …, 2021 - ieeexplore.ieee.org
The thriving of artificial intelligence (AI) applications is driving the further evolution of
wireless networks. It has been envisioned that 6G will be transformative and will …
wireless networks. It has been envisioned that 6G will be transformative and will …
Edge intelligence: On-demand deep learning model co-inference with device-edge synergy
As the backbone technology of machine learning, deep neural networks (DNNs) have have
quickly ascended to the spotlight. Running DNNs on resource-constrained mobile devices …
quickly ascended to the spotlight. Running DNNs on resource-constrained mobile devices …