CoHOG: A Light-Weight, Compute-Efficient, and Training-Free Visual Place Recognition Technique for Changing Environments
Sign Up to like & getrecommendations! Published in 2020 at "IEEE Robotics and Automation Letters"
DOI: 10.1109/lra.2020.2969917
Abstract: This letter presents a novel, compute-efficient and training-free approach based on Histogram-of-Oriented-Gradients (HOG) descriptor for achieving state-of-the-art performance-per-compute-unit in Visual Place Recognition (VPR). The inspiration for this approach (namely CoHOG) is based on the convolutional… read more here.
Keywords: place recognition; place; training free; efficient training ... See more keywords
ETA: An Efficient Training Accelerator for DNNs Based on Hardware-Algorithm Co-Optimization.
Sign Up to like & getrecommendations! Published in 2022 at "IEEE transactions on neural networks and learning systems"
DOI: 10.1109/tnnls.2022.3145850
Abstract: Recently, the efficient training of deep neural networks (DNNs) on resource-constrained platforms has attracted increasing attention for protecting user privacy. However, it is still a severe challenge since the DNN training involves intensive computations and… read more here.
Keywords: hardware; efficient training; algorithm optimization; hardware algorithm ... See more keywords
Efficient Training Data Acquisition Technique for Deep Learning Networks in Radar Applications
Sign Up to like & getrecommendations! Published in 2024 at "Journal of Electromagnetic Engineering and Science"
DOI: 10.26866/jees.2024.5.r.246
Abstract: In the field of radar, deep learning techniques have shown considerably superior performance over traditional classifiers in detecting and classifying targets. However, acquiring sufficient training data for deep learning applications is often challenging and time… read more here.
Keywords: deep learning; data acquisition; training data; efficient training ... See more keywords