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Found 8 papers, 2 papers with code
PQV-Mobile: A Combined Pruning and Quantization Toolkit to Optimize Vision Transformers for Mobile Applications
Our results show that even pruning a DeiT model by 9. 375% and quantizing it to int8 from FP32 followed by optimizing for mobile applications, we find a latency reduction by 7. 18X with a small accuracy loss of 2. 24%.
Scaling Compute Is Not All You Need for Adversarial Robustness
Finally, we make our benchmarking framework (built on top of \texttt{timm}~\citep{rw2019timm}) publicly available to facilitate future analysis in efficient robust deep learning.
Machine Learning-Enhanced Prediction of Surface Smoothness for Inertial Confinement Fusion Target Polishing Using Limited Data
In Inertial Confinement Fusion (ICF) process, roughly a 2mm spherical shell made of high density carbon is used as target for laser beams, which compress and heat it to energy levels needed for high fusion yield.
Real-Time Fully Unsupervised Domain Adaptation for Lane Detection in Autonomous Driving
While deep neural networks are being utilized heavily for autonomous driving, they need to be adapted to new unseen environmental conditions for which they were not trained.
Benchmarking Test-Time Unsupervised Deep Neural Network Adaptation on Edge Devices
To improve robustness of DNNs, they must be able to update themselves to enhance their prediction accuracy.
Semi-supervised on-device neural network adaptation for remote and portable laser-induced breakdown spectroscopy
However, ML for LIBS is challenging as: (i) the predictive models must be lightweight since they need to be deployed in highly resource-constrained and battery-operated portable LIBS systems; and (ii) since these systems can be remote, the models must be able to self-adapt to any domain shift in input distributions which could be due to the lack of different types of inputs in training data or dynamic environmental/sensor noise.
AutoPilot: Automating SoC Design Space Exploration for SWaP Constrained Autonomous UAVs
Balancing a computing system for a UAV requires considering both the cyber (e. g., sensor rate, compute performance) and physical (e. g., payload weight) characteristics that affect overall performance.
SMAUG: End-to-End Full-Stack Simulation Infrastructure for Deep Learning Workloads
In recent years, there has been tremendous advances in hardware acceleration of deep neural networks.
