Scheduling real-time deep learning services as imprecise computations
The paper presents a real-time computing framework for intelligent real-time edge services,
on behalf of local embedded devices that are themselves unable to support extensive …
on behalf of local embedded devices that are themselves unable to support extensive …
Deeprt: A soft real time scheduler for computer vision applications on the edge
The ubiquity of smartphone cameras and IoT cameras, together with the recent boom of
deep learning and deep neural networks, proliferate various computer vision driven mobile …
deep learning and deep neural networks, proliferate various computer vision driven mobile …
Lalarand: Flexible layer-by-layer cpu/gpu scheduling for real-time dnn tasks
Deep neural networks (DNNs) have shown remarkable success in various machine-learning
(ML) tasks useful for many safety-critical, real-time embedded systems. The foremost design …
(ML) tasks useful for many safety-critical, real-time embedded systems. The foremost design …
Pipelined data-parallel CPU/GPU scheduling for multi-DNN real-time inference
Deep neural networks (DNNs) have been showing significant success in various
applications, such as autonomous driving, mobile devices, and Internet of Things. Although …
applications, such as autonomous driving, mobile devices, and Internet of Things. Although …
Apnet: Approximation-aware real-time neural network
Modern embedded cyber-physical systems are becoming entangled with the realm of deep
neural networks (DNNs) towards increased autonomy. While applying DNNs can …
neural networks (DNNs) towards increased autonomy. While applying DNNs can …
On removing algorithmic priority inversion from mission-critical machine inference pipelines
The paper discusses algorithmic priority inversion in mission-critical machine inference
pipelines used in modern neural-network-based cyber-physical applications, and develops …
pipelines used in modern neural-network-based cyber-physical applications, and develops …
SEE: Scheduling early exit for mobile DNN inference during service outage
In recent years, the rapid development of edge computing enables us to process a wide
variety of intelligent applications at the edge, such as real-time video analytics. However …
variety of intelligent applications at the edge, such as real-time video analytics. However …
Qos-aware scheduling of heterogeneous servers for inference in deep neural networks
Deep neural networks (DNNs) are popular in diverse fields such as computer vision and
natural language processing. DNN inference tasks are emerging as a service provided by …
natural language processing. DNN inference tasks are emerging as a service provided by …
Zygarde: Time-sensitive on-device deep inference and adaptation on intermittently-powered systems
We propose Zygarde--which is an energy--and accuracy-aware soft real-time task
scheduling framework for batteryless systems that flexibly execute deep learning tasks1 that …
scheduling framework for batteryless systems that flexibly execute deep learning tasks1 that …
Prema: A predictive multi-task scheduling algorithm for preemptible neural processing units
To amortize cost, cloud vendors providing DNN acceleration as a service to end-users
employ consolidation and virtualization to share the underlying resources among multiple …
employ consolidation and virtualization to share the underlying resources among multiple …