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Found 7 papers, 1 papers with code
NAWQ-SR: A Hybrid-Precision NPU Engine for Efficient On-Device Super-Resolution
In recent years, image and video delivery systems have begun integrating deep learning super-resolution (SR) approaches, leveraging their unprecedented visual enhancement capabilities while reducing reliance on networking conditions.
Smart at what cost? Characterising Mobile Deep Neural Networks in the wild
To this end, we analyse over 16k of the most popular apps in the Google Play Store to characterise their DNN usage and performance across devices of different capabilities, both across tiers and generations.
DynO: Dynamic Onloading of Deep Neural Networks from Cloud to Device
Recently, there has been an explosive growth of mobile and embedded applications using convolutional neural networks(CNNs).
FjORD: Fair and Accurate Federated Learning under heterogeneous targets with Ordered Dropout
FjORD alleviates the problem of client system heterogeneity by tailoring the model width to the client's capabilities.
SPINN: Synergistic Progressive Inference of Neural Networks over Device and Cloud
Despite the soaring use of convolutional neural networks (CNNs) in mobile applications, uniformly sustaining high-performance inference on mobile has been elusive due to the excessive computational demands of modern CNNs and the increasing diversity of deployed devices.
EmBench: Quantifying Performance Variations of Deep Neural Networks across Modern Commodity Devices
In recent years, advances in deep learning have resulted in unprecedented leaps in diverse tasks spanning from speech and object recognition to context awareness and health monitoring.
A Family of Droids -- Android Malware Detection via Behavioral Modeling: Static vs Dynamic Analysis
Aiming to counter them, detection techniques based on either static or dynamic analysis that model Android malware, have been proposed.
Cryptography and Security
