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Found 10 papers, 1 papers with code
SwapNet: Efficient Swapping for DNN Inference on Edge AI Devices Beyond the Memory Budget
To this end, we develop SwapNet, an efficient DNN block swapping middleware for edge AI devices.
Enabling Resource-efficient AIoT System with Cross-level Optimization: A survey
Accordingly, the accuracy and responsiveness of DL models are bounded by resource availability.
Localised Adaptive Spatial-Temporal Graph Neural Network
Spatial-temporal graph models are prevailing for abstracting and modelling spatial and temporal dependencies.
POSGen: Personalized Opening Sentence Generation for Online Insurance Sales
Such personalized opening sentence generation is challenging because (i) there are limited historical samples for conversation topic recommendation in online insurance sales and (ii) existing text generation schemes often fail to support customized topic ordering based on user preferences.
AdaEnlight: Energy-aware Low-light Video Stream Enhancement on Mobile Devices
We report extensive experiments on diverse datasets, scenarios, and platforms and demonstrate the superiority of AdaEnlight compared with state-of-the-art low-light image and video enhancement solutions.
p-Meta: Towards On-device Deep Model Adaptation
Data collected by IoT devices are often private and have a large diversity across users.
Learning to Assign: Towards Fair Task Assignment in Large-Scale Ride Hailing
Ride hailing is a widespread shared mobility application where the central issue is to assign taxi requests to drivers with various objectives.
Adaptive Loss-aware Quantization for Multi-bit Networks
We investigate the compression of deep neural networks by quantizing their weights and activations into multiple binary bases, known as multi-bit networks (MBNs), which accelerate the inference and reduce the storage for the deployment on low-resource mobile and embedded platforms.
Pruning-Aware Merging for Efficient Multitask Inference
Given a set of deep neural networks, each pre-trained for a single task, it is desired that executing arbitrary combinations of tasks yields minimal computation cost.
Multi-Task Zipping via Layer-wise Neuron Sharing
Future mobile devices are anticipated to perceive, understand and react to the world on their own by running multiple correlated deep neural networks on-device.
