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 …
HiTDL: High-throughput deep learning inference at the hybrid mobile edge
Deep neural networks (DNNs) have become a critical component for inference in modern
mobile applications, but the efficient provisioning of DNNs is non-trivial. Existing mobile-and …
mobile applications, but the efficient provisioning of DNNs is non-trivial. Existing mobile-and …
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 …
MNN: A universal and efficient inference engine
X Jiang, H Wang, Y Chen, Z Wu… - Proceedings of …, 2020 - proceedings.mlsys.org
Deploying deep learning (DL) models on mobile devices draws more and more attention
recently. However, designing an efficient inference engine on devices is under the great …
recently. However, designing an efficient inference engine on devices is under the great …
An adaptive DNN inference acceleration framework with end–edge–cloud collaborative computing
Abstract Deep Neural Networks (DNNs) based on intelligent applications have been
intensively deployed on mobile devices. Unfortunately, resource-constrained mobile devices …
intensively deployed on mobile devices. Unfortunately, resource-constrained mobile devices …
Band: coordinated multi-dnn inference on heterogeneous mobile processors
The rapid development of deep learning algorithms, as well as innovative hardware
advancements, encourages multi-DNN workloads such as augmented reality applications …
advancements, encourages multi-DNN workloads such as augmented reality applications …
Enable pipeline processing of DNN co-inference tasks in the mobile-edge cloud
Deep Neural Network (DNN) based artificial intelligence help driving the great development
of mobile Internet. However, the hardware of a mobile device may not be sufficiently to meet …
of mobile Internet. However, the hardware of a mobile device may not be sufficiently to meet …
Mdinference: Balancing inference accuracy and latency for mobile applications
Deep Neural Networks are allowing mobile devices to incorporate a wide range of features
into user applications. However, the computational complexity of these models makes it …
into user applications. However, the computational complexity of these models makes it …
Computation offloading scheduling for deep neural network inference in mobile computing
The quality of service (QoS) of intelligent applications on mobile devices heavily depends on
the inference speed of Deep Neural Network (DNN) models. Cooperative DNN inference …
the inference speed of Deep Neural Network (DNN) models. Cooperative DNN inference …