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Found 8 papers, 2 papers with code
RED: A Systematic Real-Time Scheduling Approach for Robotic Environmental Dynamics
This process ensures the scheduling framework's compatibility with MIMONet and maximizes efficiency.
Minimalist Traffic Prediction: Linear Layer Is All You Need
Traffic prediction is essential for the progression of Intelligent Transportation Systems (ITS) and the vision of smart cities.
MIMONet: Multi-Input Multi-Output On-Device Deep Learning
In this paper, we propose MIMONet, a novel on-device multi-input multi-output (MIMO) DNN framework that achieves high accuracy and on-device efficiency in terms of critical performance metrics such as latency, energy, and memory usage.
Localised Adaptive Spatial-Temporal Graph Neural Network
Spatial-temporal graph models are prevailing for abstracting and modelling spatial and temporal dependencies.
Subspace-Configurable Networks
The obtained subspace is low-dimensional and has a surprisingly simple structure even for complex, non-invertible transformations of the input, leading to an exceptionally high efficiency of subspace-configurable networks (SCNs) when limited storage and computing resources are at stake.
Combating Distribution Shift for Accurate Time Series Forecasting via Hypernetworks
In this paper, we propose Hyper Time- Series Forecasting (HTSF), a hypernetwork-based framework for accurate time series forecasting under distribution shift.
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.
