Improving device-edge cooperative inference of deep learning via 2-step pruning
Deep neural networks (DNNs) are state-of-the-art solutions for many machine learning
applications, and have been widely used on mobile devices. Running DNNs on …
applications, and have been widely used on mobile devices. Running DNNs on …
Edge AI: On-demand accelerating deep neural network inference via edge computing
As a key technology of enabling Artificial Intelligence (AI) applications in 5G era, Deep
Neural Networks (DNNs) have quickly attracted widespread attention. However, it is …
Neural Networks (DNNs) have quickly attracted widespread attention. However, it is …
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 …
Multi-agent collaborative inference via dnn decoupling: Intermediate feature compression and edge learning
Z Hao, G Xu, Y Luo, H Hu, J An… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Recently, deploying deep neural network (DNN) models via collaborative inference, which
splits a pre-trained model into two parts and executes them on user equipment (UE) and …
splits a pre-trained model into two parts and executes them on user equipment (UE) and …
Auto-tuning neural network quantization framework for collaborative inference between the cloud and edge
Recently, deep neural networks (DNNs) have been widely applied in mobile intelligent
applications. The inference for the DNNs is usually performed in the cloud. However, it leads …
applications. The inference for the DNNs is usually performed in the cloud. However, it leads …
Toward collaborative inferencing of deep neural networks on Internet-of-Things devices
Recent advancements in deep neural networks (DNNs) have enabled us to solve
traditionally challenging problems. To deploy a service based on DNNs, since DNNs are …
traditionally challenging problems. To deploy a service based on DNNs, since DNNs are …
Towards real-time cooperative deep inference over the cloud and edge end devices
Deep neural networks (DNNs) have been widely used in many intelligent applications such
as object recognition and automatic driving due to their superior performance in conducting …
as object recognition and automatic driving due to their superior performance in conducting …
Graft: Efficient inference serving for hybrid deep learning with SLO guarantees via DNN re-alignment
Deep neural networks (DNNs) have been widely adopted for various mobile inference tasks,
yet their ever-increasing computational demands are hindering their deployment on …
yet their ever-increasing computational demands are hindering their deployment on …
Optimal model placement and online model splitting for device-edge co-inference
Device-edge co-inference opens up new possibilities for resource-constrained wireless
devices (WDs) to execute deep neural network (DNN)-based applications with heavy …
devices (WDs) to execute deep neural network (DNN)-based applications with heavy …
Coedge: Cooperative dnn inference with adaptive workload partitioning over heterogeneous edge devices
Recent advances in artificial intelligence have driven increasing intelligent applications at
the network edge, such as smart home, smart factory, and smart city. To deploy …
the network edge, such as smart home, smart factory, and smart city. To deploy …